{
 "cells": [
  {
   "cell_type": "markdown",
   "id": "fc14d404",
   "metadata": {},
   "source": [
    "### 高中体测数据转换"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "9ad60900",
   "metadata": {},
   "outputs": [],
   "source": [
    "import numpy as np\n",
    "import pandas as pd\n",
    "import matplotlib.pyplot as plt"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "ce231335",
   "metadata": {},
   "source": [
    "### 1、数据加载， pd.read_excel('./18级高一体测成绩汇总.xls')默认加载第一个工作表\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "0d97bb8b",
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>班级</th>\n",
       "      <th>性别</th>\n",
       "      <th>男1000米跑</th>\n",
       "      <th>男50米跑</th>\n",
       "      <th>男跳远</th>\n",
       "      <th>男体前屈</th>\n",
       "      <th>男引体</th>\n",
       "      <th>男肺活量</th>\n",
       "      <th>身高</th>\n",
       "      <th>体重</th>\n",
       "      <th>BMI</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
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       "      <th>0</th>\n",
       "      <td>1</td>\n",
       "      <td>男</td>\n",
       "      <td>4'13</td>\n",
       "      <td>8.88</td>\n",
       "      <td>195.0</td>\n",
       "      <td>12</td>\n",
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       "      <td>170.0</td>\n",
       "      <td>72.6</td>\n",
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       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1</td>\n",
       "      <td>男</td>\n",
       "      <td>4'16</td>\n",
       "      <td>7.70</td>\n",
       "      <td>225.0</td>\n",
       "      <td>11</td>\n",
       "      <td>7</td>\n",
       "      <td>3133</td>\n",
       "      <td>174.0</td>\n",
       "      <td>52.7</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>1</td>\n",
       "      <td>男</td>\n",
       "      <td>4'09</td>\n",
       "      <td>8.45</td>\n",
       "      <td>218.0</td>\n",
       "      <td>14</td>\n",
       "      <td>1</td>\n",
       "      <td>3901</td>\n",
       "      <td>169.0</td>\n",
       "      <td>46.5</td>\n",
       "      <td>0</td>\n",
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       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>1</td>\n",
       "      <td>男</td>\n",
       "      <td>4'21</td>\n",
       "      <td>8.05</td>\n",
       "      <td>206.0</td>\n",
       "      <td>13</td>\n",
       "      <td>1</td>\n",
       "      <td>4946</td>\n",
       "      <td>183.0</td>\n",
       "      <td>79.7</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>1</td>\n",
       "      <td>男</td>\n",
       "      <td>3'44</td>\n",
       "      <td>7.52</td>\n",
       "      <td>210.0</td>\n",
       "      <td>13</td>\n",
       "      <td>9</td>\n",
       "      <td>3538</td>\n",
       "      <td>171.0</td>\n",
       "      <td>54.7</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>472</th>\n",
       "      <td>17</td>\n",
       "      <td>男</td>\n",
       "      <td>4'23</td>\n",
       "      <td>8.27</td>\n",
       "      <td>208.0</td>\n",
       "      <td>10</td>\n",
       "      <td>0</td>\n",
       "      <td>4647</td>\n",
       "      <td>176.0</td>\n",
       "      <td>69.5</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>473</th>\n",
       "      <td>17</td>\n",
       "      <td>男</td>\n",
       "      <td>5'19</td>\n",
       "      <td>9.55</td>\n",
       "      <td>210.0</td>\n",
       "      <td>15</td>\n",
       "      <td>6</td>\n",
       "      <td>7042</td>\n",
       "      <td>177.0</td>\n",
       "      <td>76.0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>474</th>\n",
       "      <td>17</td>\n",
       "      <td>男</td>\n",
       "      <td>3'25</td>\n",
       "      <td>7.50</td>\n",
       "      <td>252.0</td>\n",
       "      <td>13</td>\n",
       "      <td>13</td>\n",
       "      <td>5755</td>\n",
       "      <td>181.0</td>\n",
       "      <td>65.0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>475</th>\n",
       "      <td>17</td>\n",
       "      <td>男</td>\n",
       "      <td>4'39</td>\n",
       "      <td>7.81</td>\n",
       "      <td>208.0</td>\n",
       "      <td>14</td>\n",
       "      <td>11</td>\n",
       "      <td>5688</td>\n",
       "      <td>172.0</td>\n",
       "      <td>51.7</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>476</th>\n",
       "      <td>17</td>\n",
       "      <td>男</td>\n",
       "      <td>0</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>477 rows × 11 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "     班级 性别 男1000米跑  男50米跑    男跳远  男体前屈  男引体  男肺活量     身高    体重  BMI\n",
       "0     1  男    4'13   8.88  195.0    12    1  2785  170.0  72.6    0\n",
       "1     1  男    4'16   7.70  225.0    11    7  3133  174.0  52.7    0\n",
       "2     1  男    4'09   8.45  218.0    14    1  3901  169.0  46.5    0\n",
       "3     1  男    4'21   8.05  206.0    13    1  4946  183.0  79.7    0\n",
       "4     1  男    3'44   7.52  210.0    13    9  3538  171.0  54.7    0\n",
       "..   .. ..     ...    ...    ...   ...  ...   ...    ...   ...  ...\n",
       "472  17  男    4'23   8.27  208.0    10    0  4647  176.0  69.5    0\n",
       "473  17  男    5'19   9.55  210.0    15    6  7042  177.0  76.0    0\n",
       "474  17  男    3'25   7.50  252.0    13   13  5755  181.0  65.0    0\n",
       "475  17  男    4'39   7.81  208.0    14   11  5688  172.0  51.7    0\n",
       "476  17  男       0   0.00    0.0     0    0     0    0.0   0.0    0\n",
       "\n",
       "[477 rows x 11 columns]"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "boy=pd.read_excel('./18级高一体测成绩汇总.xls')\n",
    "boy"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "b5c0799c",
   "metadata": {},
   "source": [
    "### 2、数据加载， pd.read_excel('./18级高一体测成绩汇总.xls',sheet_name = 1)指定加载第二个工作表"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "f151210c",
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "      <th>性别</th>\n",
       "      <th>女800米跑</th>\n",
       "      <th>女50米跑</th>\n",
       "      <th>女跳远</th>\n",
       "      <th>女体前屈</th>\n",
       "      <th>女仰卧</th>\n",
       "      <th>女肺活量</th>\n",
       "      <th>身高</th>\n",
       "      <th>体重</th>\n",
       "      <th>BMI</th>\n",
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       "  </thead>\n",
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       "      <td>3.22</td>\n",
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       "      <td>4.59</td>\n",
       "      <td>11.44</td>\n",
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       "      <td>9</td>\n",
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       "      <td>3683</td>\n",
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       "      <td>66.6</td>\n",
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       "    <tr>\n",
       "      <th>2</th>\n",
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       "      <td>女</td>\n",
       "      <td>3.46</td>\n",
       "      <td>13.40</td>\n",
       "      <td>150.0</td>\n",
       "      <td>7</td>\n",
       "      <td>40</td>\n",
       "      <td>3331</td>\n",
       "      <td>157.0</td>\n",
       "      <td>60.0</td>\n",
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       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>1</td>\n",
       "      <td>女</td>\n",
       "      <td>3.39</td>\n",
       "      <td>9.52</td>\n",
       "      <td>172.0</td>\n",
       "      <td>21</td>\n",
       "      <td>46</td>\n",
       "      <td>3701</td>\n",
       "      <td>160.0</td>\n",
       "      <td>50.7</td>\n",
       "      <td>0</td>\n",
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       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>1</td>\n",
       "      <td>女</td>\n",
       "      <td>3.43</td>\n",
       "      <td>9.79</td>\n",
       "      <td>145.0</td>\n",
       "      <td>8</td>\n",
       "      <td>34</td>\n",
       "      <td>3592</td>\n",
       "      <td>167.0</td>\n",
       "      <td>63.9</td>\n",
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       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
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       "    <tr>\n",
       "      <th>588</th>\n",
       "      <td>17</td>\n",
       "      <td>女</td>\n",
       "      <td>3.51</td>\n",
       "      <td>9.60</td>\n",
       "      <td>150.0</td>\n",
       "      <td>24</td>\n",
       "      <td>41</td>\n",
       "      <td>2255</td>\n",
       "      <td>158.0</td>\n",
       "      <td>49.0</td>\n",
       "      <td>0</td>\n",
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       "      <th>589</th>\n",
       "      <td>17</td>\n",
       "      <td>女</td>\n",
       "      <td>4.00</td>\n",
       "      <td>10.18</td>\n",
       "      <td>150.0</td>\n",
       "      <td>13</td>\n",
       "      <td>36</td>\n",
       "      <td>2937</td>\n",
       "      <td>161.0</td>\n",
       "      <td>55.7</td>\n",
       "      <td>0</td>\n",
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       "    <tr>\n",
       "      <th>590</th>\n",
       "      <td>17</td>\n",
       "      <td>女</td>\n",
       "      <td>3.45</td>\n",
       "      <td>10.18</td>\n",
       "      <td>152.0</td>\n",
       "      <td>15</td>\n",
       "      <td>35</td>\n",
       "      <td>2592</td>\n",
       "      <td>165.0</td>\n",
       "      <td>48.6</td>\n",
       "      <td>0</td>\n",
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       "    <tr>\n",
       "      <th>591</th>\n",
       "      <td>17</td>\n",
       "      <td>女</td>\n",
       "      <td>4.01</td>\n",
       "      <td>9.67</td>\n",
       "      <td>165.0</td>\n",
       "      <td>10</td>\n",
       "      <td>41</td>\n",
       "      <td>1829</td>\n",
       "      <td>154.0</td>\n",
       "      <td>43.6</td>\n",
       "      <td>0</td>\n",
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       "      <th>592</th>\n",
       "      <td>17</td>\n",
       "      <td>女</td>\n",
       "      <td>4.48</td>\n",
       "      <td>9.09</td>\n",
       "      <td>180.0</td>\n",
       "      <td>10</td>\n",
       "      <td>46</td>\n",
       "      <td>2962</td>\n",
       "      <td>162.0</td>\n",
       "      <td>55.3</td>\n",
       "      <td>0</td>\n",
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       "</table>\n",
       "<p>593 rows × 11 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "     班级 性别  女800米跑  女50米跑    女跳远  女体前屈  女仰卧  女肺活量     身高    体重  BMI\n",
       "0     1  女    3.22   9.32  185.0    16   48  3775  163.0  51.3    0\n",
       "1     1  女    4.59  11.44  148.0     9   29  3683  163.0  66.6    0\n",
       "2     1  女    3.46  13.40  150.0     7   40  3331  157.0  60.0    0\n",
       "3     1  女    3.39   9.52  172.0    21   46  3701  160.0  50.7    0\n",
       "4     1  女    3.43   9.79  145.0     8   34  3592  167.0  63.9    0\n",
       "..   .. ..     ...    ...    ...   ...  ...   ...    ...   ...  ...\n",
       "588  17  女    3.51   9.60  150.0    24   41  2255  158.0  49.0    0\n",
       "589  17  女    4.00  10.18  150.0    13   36  2937  161.0  55.7    0\n",
       "590  17  女    3.45  10.18  152.0    15   35  2592  165.0  48.6    0\n",
       "591  17  女    4.01   9.67  165.0    10   41  1829  154.0  43.6    0\n",
       "592  17  女    4.48   9.09  180.0    10   46  2962  162.0  55.3    0\n",
       "\n",
       "[593 rows x 11 columns]"
      ]
     },
     "execution_count": 2,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "girl=pd.read_excel('./18级高一体测成绩汇总.xls',sheet_name = 1)\n",
    "girl"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "5dda6eea",
   "metadata": {},
   "source": [
    "### 3、评分标准加载，pd.read_excel('./体侧成绩评分表.xls',header = [0,1])，header=[0,1]表示多层列索引"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "c71150fc",
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "      <th colspan=\"2\" halign=\"left\">女肺活量</th>\n",
       "      <th colspan=\"2\" halign=\"left\">男50米跑</th>\n",
       "      <th colspan=\"2\" halign=\"left\">女50米跑</th>\n",
       "      <th colspan=\"2\" halign=\"left\">男体前屈</th>\n",
       "      <th>...</th>\n",
       "      <th colspan=\"2\" halign=\"left\">女跳远</th>\n",
       "      <th colspan=\"2\" halign=\"left\">男引体</th>\n",
       "      <th colspan=\"2\" halign=\"left\">女仰卧</th>\n",
       "      <th colspan=\"2\" halign=\"left\">男1000米跑</th>\n",
       "      <th colspan=\"2\" halign=\"left\">女800米跑</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th></th>\n",
       "      <th>成绩</th>\n",
       "      <th>分数</th>\n",
       "      <th>成绩</th>\n",
       "      <th>分数</th>\n",
       "      <th>成绩</th>\n",
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       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>4540</td>\n",
       "      <td>100</td>\n",
       "      <td>3150</td>\n",
       "      <td>100</td>\n",
       "      <td>7.1</td>\n",
       "      <td>100</td>\n",
       "      <td>7.8</td>\n",
       "      <td>100</td>\n",
       "      <td>23.6</td>\n",
       "      <td>100</td>\n",
       "      <td>...</td>\n",
       "      <td>204</td>\n",
       "      <td>100</td>\n",
       "      <td>16.0</td>\n",
       "      <td>100</td>\n",
       "      <td>53</td>\n",
       "      <td>100</td>\n",
       "      <td>3'30\"</td>\n",
       "      <td>100</td>\n",
       "      <td>3'24\"</td>\n",
       "      <td>100</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>4420</td>\n",
       "      <td>95</td>\n",
       "      <td>3100</td>\n",
       "      <td>95</td>\n",
       "      <td>7.2</td>\n",
       "      <td>95</td>\n",
       "      <td>7.9</td>\n",
       "      <td>95</td>\n",
       "      <td>21.5</td>\n",
       "      <td>95</td>\n",
       "      <td>...</td>\n",
       "      <td>198</td>\n",
       "      <td>95</td>\n",
       "      <td>15.0</td>\n",
       "      <td>95</td>\n",
       "      <td>51</td>\n",
       "      <td>95</td>\n",
       "      <td>3'35\"</td>\n",
       "      <td>95</td>\n",
       "      <td>3'30\"</td>\n",
       "      <td>95</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>4300</td>\n",
       "      <td>90</td>\n",
       "      <td>3050</td>\n",
       "      <td>90</td>\n",
       "      <td>7.3</td>\n",
       "      <td>90</td>\n",
       "      <td>8.0</td>\n",
       "      <td>90</td>\n",
       "      <td>19.4</td>\n",
       "      <td>90</td>\n",
       "      <td>...</td>\n",
       "      <td>192</td>\n",
       "      <td>90</td>\n",
       "      <td>14.0</td>\n",
       "      <td>90</td>\n",
       "      <td>49</td>\n",
       "      <td>90</td>\n",
       "      <td>3'40\"</td>\n",
       "      <td>90</td>\n",
       "      <td>3'36\"</td>\n",
       "      <td>90</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>4050</td>\n",
       "      <td>85</td>\n",
       "      <td>2900</td>\n",
       "      <td>85</td>\n",
       "      <td>7.4</td>\n",
       "      <td>85</td>\n",
       "      <td>8.3</td>\n",
       "      <td>85</td>\n",
       "      <td>17.2</td>\n",
       "      <td>85</td>\n",
       "      <td>...</td>\n",
       "      <td>185</td>\n",
       "      <td>85</td>\n",
       "      <td>13.0</td>\n",
       "      <td>85</td>\n",
       "      <td>46</td>\n",
       "      <td>85</td>\n",
       "      <td>3'47\"</td>\n",
       "      <td>85</td>\n",
       "      <td>3'43\"</td>\n",
       "      <td>85</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>3800</td>\n",
       "      <td>80</td>\n",
       "      <td>2750</td>\n",
       "      <td>80</td>\n",
       "      <td>7.5</td>\n",
       "      <td>80</td>\n",
       "      <td>8.6</td>\n",
       "      <td>80</td>\n",
       "      <td>15.0</td>\n",
       "      <td>80</td>\n",
       "      <td>...</td>\n",
       "      <td>178</td>\n",
       "      <td>80</td>\n",
       "      <td>12.0</td>\n",
       "      <td>80</td>\n",
       "      <td>43</td>\n",
       "      <td>80</td>\n",
       "      <td>3'55\"</td>\n",
       "      <td>80</td>\n",
       "      <td>3'50\"</td>\n",
       "      <td>80</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>3680</td>\n",
       "      <td>78</td>\n",
       "      <td>2650</td>\n",
       "      <td>78</td>\n",
       "      <td>7.7</td>\n",
       "      <td>78</td>\n",
       "      <td>8.8</td>\n",
       "      <td>78</td>\n",
       "      <td>13.6</td>\n",
       "      <td>78</td>\n",
       "      <td>...</td>\n",
       "      <td>175</td>\n",
       "      <td>78</td>\n",
       "      <td>NaN</td>\n",
       "      <td>78</td>\n",
       "      <td>41</td>\n",
       "      <td>78</td>\n",
       "      <td>4'00\"</td>\n",
       "      <td>78</td>\n",
       "      <td>3'55\"</td>\n",
       "      <td>78</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>3560</td>\n",
       "      <td>76</td>\n",
       "      <td>2550</td>\n",
       "      <td>76</td>\n",
       "      <td>7.9</td>\n",
       "      <td>76</td>\n",
       "      <td>9.0</td>\n",
       "      <td>76</td>\n",
       "      <td>12.2</td>\n",
       "      <td>76</td>\n",
       "      <td>...</td>\n",
       "      <td>172</td>\n",
       "      <td>76</td>\n",
       "      <td>11.0</td>\n",
       "      <td>76</td>\n",
       "      <td>39</td>\n",
       "      <td>76</td>\n",
       "      <td>4'05\"</td>\n",
       "      <td>76</td>\n",
       "      <td>4'00\"</td>\n",
       "      <td>76</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>3440</td>\n",
       "      <td>74</td>\n",
       "      <td>2450</td>\n",
       "      <td>74</td>\n",
       "      <td>8.1</td>\n",
       "      <td>74</td>\n",
       "      <td>9.2</td>\n",
       "      <td>74</td>\n",
       "      <td>10.8</td>\n",
       "      <td>74</td>\n",
       "      <td>...</td>\n",
       "      <td>169</td>\n",
       "      <td>74</td>\n",
       "      <td>NaN</td>\n",
       "      <td>74</td>\n",
       "      <td>37</td>\n",
       "      <td>74</td>\n",
       "      <td>4'10\"</td>\n",
       "      <td>74</td>\n",
       "      <td>4'05\"</td>\n",
       "      <td>74</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>3320</td>\n",
       "      <td>72</td>\n",
       "      <td>2350</td>\n",
       "      <td>72</td>\n",
       "      <td>8.3</td>\n",
       "      <td>72</td>\n",
       "      <td>9.4</td>\n",
       "      <td>72</td>\n",
       "      <td>9.4</td>\n",
       "      <td>72</td>\n",
       "      <td>...</td>\n",
       "      <td>166</td>\n",
       "      <td>72</td>\n",
       "      <td>10.0</td>\n",
       "      <td>72</td>\n",
       "      <td>35</td>\n",
       "      <td>72</td>\n",
       "      <td>4'15\"</td>\n",
       "      <td>72</td>\n",
       "      <td>4'10\"</td>\n",
       "      <td>72</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>3200</td>\n",
       "      <td>70</td>\n",
       "      <td>2250</td>\n",
       "      <td>70</td>\n",
       "      <td>8.5</td>\n",
       "      <td>70</td>\n",
       "      <td>9.6</td>\n",
       "      <td>70</td>\n",
       "      <td>8.0</td>\n",
       "      <td>70</td>\n",
       "      <td>...</td>\n",
       "      <td>163</td>\n",
       "      <td>70</td>\n",
       "      <td>NaN</td>\n",
       "      <td>70</td>\n",
       "      <td>33</td>\n",
       "      <td>70</td>\n",
       "      <td>4'20\"</td>\n",
       "      <td>70</td>\n",
       "      <td>4'15\"</td>\n",
       "      <td>70</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>3080</td>\n",
       "      <td>68</td>\n",
       "      <td>2150</td>\n",
       "      <td>68</td>\n",
       "      <td>8.7</td>\n",
       "      <td>68</td>\n",
       "      <td>9.8</td>\n",
       "      <td>68</td>\n",
       "      <td>6.6</td>\n",
       "      <td>68</td>\n",
       "      <td>...</td>\n",
       "      <td>160</td>\n",
       "      <td>68</td>\n",
       "      <td>9.0</td>\n",
       "      <td>68</td>\n",
       "      <td>31</td>\n",
       "      <td>68</td>\n",
       "      <td>4'25\"</td>\n",
       "      <td>68</td>\n",
       "      <td>4'20\"</td>\n",
       "      <td>68</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>2960</td>\n",
       "      <td>66</td>\n",
       "      <td>2050</td>\n",
       "      <td>66</td>\n",
       "      <td>8.9</td>\n",
       "      <td>66</td>\n",
       "      <td>10.0</td>\n",
       "      <td>66</td>\n",
       "      <td>5.2</td>\n",
       "      <td>66</td>\n",
       "      <td>...</td>\n",
       "      <td>157</td>\n",
       "      <td>66</td>\n",
       "      <td>NaN</td>\n",
       "      <td>66</td>\n",
       "      <td>29</td>\n",
       "      <td>66</td>\n",
       "      <td>4'30\"</td>\n",
       "      <td>66</td>\n",
       "      <td>4'25\"</td>\n",
       "      <td>66</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>2840</td>\n",
       "      <td>64</td>\n",
       "      <td>1950</td>\n",
       "      <td>64</td>\n",
       "      <td>9.1</td>\n",
       "      <td>64</td>\n",
       "      <td>10.2</td>\n",
       "      <td>64</td>\n",
       "      <td>3.8</td>\n",
       "      <td>64</td>\n",
       "      <td>...</td>\n",
       "      <td>154</td>\n",
       "      <td>64</td>\n",
       "      <td>8.0</td>\n",
       "      <td>64</td>\n",
       "      <td>27</td>\n",
       "      <td>64</td>\n",
       "      <td>4'35\"</td>\n",
       "      <td>64</td>\n",
       "      <td>4'30\"</td>\n",
       "      <td>64</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13</th>\n",
       "      <td>2720</td>\n",
       "      <td>62</td>\n",
       "      <td>1850</td>\n",
       "      <td>62</td>\n",
       "      <td>9.3</td>\n",
       "      <td>62</td>\n",
       "      <td>10.4</td>\n",
       "      <td>62</td>\n",
       "      <td>2.4</td>\n",
       "      <td>62</td>\n",
       "      <td>...</td>\n",
       "      <td>151</td>\n",
       "      <td>62</td>\n",
       "      <td>NaN</td>\n",
       "      <td>62</td>\n",
       "      <td>25</td>\n",
       "      <td>62</td>\n",
       "      <td>4'40\"</td>\n",
       "      <td>62</td>\n",
       "      <td>4'35\"</td>\n",
       "      <td>62</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14</th>\n",
       "      <td>2600</td>\n",
       "      <td>60</td>\n",
       "      <td>1750</td>\n",
       "      <td>60</td>\n",
       "      <td>9.5</td>\n",
       "      <td>60</td>\n",
       "      <td>10.6</td>\n",
       "      <td>60</td>\n",
       "      <td>1.0</td>\n",
       "      <td>60</td>\n",
       "      <td>...</td>\n",
       "      <td>148</td>\n",
       "      <td>60</td>\n",
       "      <td>7.0</td>\n",
       "      <td>60</td>\n",
       "      <td>23</td>\n",
       "      <td>60</td>\n",
       "      <td>4'45\"</td>\n",
       "      <td>60</td>\n",
       "      <td>4'40\"</td>\n",
       "      <td>60</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15</th>\n",
       "      <td>2470</td>\n",
       "      <td>50</td>\n",
       "      <td>1710</td>\n",
       "      <td>50</td>\n",
       "      <td>9.7</td>\n",
       "      <td>50</td>\n",
       "      <td>10.8</td>\n",
       "      <td>50</td>\n",
       "      <td>0.0</td>\n",
       "      <td>50</td>\n",
       "      <td>...</td>\n",
       "      <td>143</td>\n",
       "      <td>50</td>\n",
       "      <td>6.0</td>\n",
       "      <td>50</td>\n",
       "      <td>21</td>\n",
       "      <td>50</td>\n",
       "      <td>5'05\"</td>\n",
       "      <td>50</td>\n",
       "      <td>4'50\"</td>\n",
       "      <td>50</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16</th>\n",
       "      <td>2340</td>\n",
       "      <td>40</td>\n",
       "      <td>1670</td>\n",
       "      <td>40</td>\n",
       "      <td>9.9</td>\n",
       "      <td>40</td>\n",
       "      <td>11.0</td>\n",
       "      <td>40</td>\n",
       "      <td>-1.0</td>\n",
       "      <td>40</td>\n",
       "      <td>...</td>\n",
       "      <td>138</td>\n",
       "      <td>40</td>\n",
       "      <td>5.0</td>\n",
       "      <td>40</td>\n",
       "      <td>19</td>\n",
       "      <td>40</td>\n",
       "      <td>5'25\"</td>\n",
       "      <td>40</td>\n",
       "      <td>5'00\"</td>\n",
       "      <td>40</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>17</th>\n",
       "      <td>2210</td>\n",
       "      <td>30</td>\n",
       "      <td>1630</td>\n",
       "      <td>30</td>\n",
       "      <td>10.1</td>\n",
       "      <td>30</td>\n",
       "      <td>11.2</td>\n",
       "      <td>30</td>\n",
       "      <td>-2.0</td>\n",
       "      <td>30</td>\n",
       "      <td>...</td>\n",
       "      <td>133</td>\n",
       "      <td>30</td>\n",
       "      <td>4.0</td>\n",
       "      <td>30</td>\n",
       "      <td>17</td>\n",
       "      <td>30</td>\n",
       "      <td>5'45\"</td>\n",
       "      <td>30</td>\n",
       "      <td>5'10\"</td>\n",
       "      <td>30</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>18</th>\n",
       "      <td>2080</td>\n",
       "      <td>20</td>\n",
       "      <td>1590</td>\n",
       "      <td>20</td>\n",
       "      <td>10.3</td>\n",
       "      <td>20</td>\n",
       "      <td>11.4</td>\n",
       "      <td>20</td>\n",
       "      <td>-3.0</td>\n",
       "      <td>20</td>\n",
       "      <td>...</td>\n",
       "      <td>128</td>\n",
       "      <td>20</td>\n",
       "      <td>3.0</td>\n",
       "      <td>20</td>\n",
       "      <td>15</td>\n",
       "      <td>20</td>\n",
       "      <td>6'05\"</td>\n",
       "      <td>20</td>\n",
       "      <td>5'20\"</td>\n",
       "      <td>20</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>19</th>\n",
       "      <td>1950</td>\n",
       "      <td>10</td>\n",
       "      <td>1550</td>\n",
       "      <td>10</td>\n",
       "      <td>10.5</td>\n",
       "      <td>10</td>\n",
       "      <td>11.6</td>\n",
       "      <td>10</td>\n",
       "      <td>-4.0</td>\n",
       "      <td>10</td>\n",
       "      <td>...</td>\n",
       "      <td>123</td>\n",
       "      <td>10</td>\n",
       "      <td>2.0</td>\n",
       "      <td>10</td>\n",
       "      <td>13</td>\n",
       "      <td>10</td>\n",
       "      <td>6'25\"</td>\n",
       "      <td>10</td>\n",
       "      <td>5'30\"</td>\n",
       "      <td>10</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>20 rows × 24 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "    男肺活量       女肺活量      男50米跑      女50米跑       男体前屈       ...  女跳远       \\\n",
       "      成绩   分数    成绩   分数    成绩   分数    成绩   分数    成绩   分数  ...   成绩   分数   \n",
       "0   4540  100  3150  100   7.1  100   7.8  100  23.6  100  ...  204  100   \n",
       "1   4420   95  3100   95   7.2   95   7.9   95  21.5   95  ...  198   95   \n",
       "2   4300   90  3050   90   7.3   90   8.0   90  19.4   90  ...  192   90   \n",
       "3   4050   85  2900   85   7.4   85   8.3   85  17.2   85  ...  185   85   \n",
       "4   3800   80  2750   80   7.5   80   8.6   80  15.0   80  ...  178   80   \n",
       "5   3680   78  2650   78   7.7   78   8.8   78  13.6   78  ...  175   78   \n",
       "6   3560   76  2550   76   7.9   76   9.0   76  12.2   76  ...  172   76   \n",
       "7   3440   74  2450   74   8.1   74   9.2   74  10.8   74  ...  169   74   \n",
       "8   3320   72  2350   72   8.3   72   9.4   72   9.4   72  ...  166   72   \n",
       "9   3200   70  2250   70   8.5   70   9.6   70   8.0   70  ...  163   70   \n",
       "10  3080   68  2150   68   8.7   68   9.8   68   6.6   68  ...  160   68   \n",
       "11  2960   66  2050   66   8.9   66  10.0   66   5.2   66  ...  157   66   \n",
       "12  2840   64  1950   64   9.1   64  10.2   64   3.8   64  ...  154   64   \n",
       "13  2720   62  1850   62   9.3   62  10.4   62   2.4   62  ...  151   62   \n",
       "14  2600   60  1750   60   9.5   60  10.6   60   1.0   60  ...  148   60   \n",
       "15  2470   50  1710   50   9.7   50  10.8   50   0.0   50  ...  143   50   \n",
       "16  2340   40  1670   40   9.9   40  11.0   40  -1.0   40  ...  138   40   \n",
       "17  2210   30  1630   30  10.1   30  11.2   30  -2.0   30  ...  133   30   \n",
       "18  2080   20  1590   20  10.3   20  11.4   20  -3.0   20  ...  128   20   \n",
       "19  1950   10  1550   10  10.5   10  11.6   10  -4.0   10  ...  123   10   \n",
       "\n",
       "     男引体      女仰卧      男1000米跑      女800米跑       \n",
       "      成绩   分数  成绩   分数      成绩   分数     成绩   分数  \n",
       "0   16.0  100  53  100   3'30\"  100  3'24\"  100  \n",
       "1   15.0   95  51   95   3'35\"   95  3'30\"   95  \n",
       "2   14.0   90  49   90   3'40\"   90  3'36\"   90  \n",
       "3   13.0   85  46   85   3'47\"   85  3'43\"   85  \n",
       "4   12.0   80  43   80   3'55\"   80  3'50\"   80  \n",
       "5    NaN   78  41   78   4'00\"   78  3'55\"   78  \n",
       "6   11.0   76  39   76   4'05\"   76  4'00\"   76  \n",
       "7    NaN   74  37   74   4'10\"   74  4'05\"   74  \n",
       "8   10.0   72  35   72   4'15\"   72  4'10\"   72  \n",
       "9    NaN   70  33   70   4'20\"   70  4'15\"   70  \n",
       "10   9.0   68  31   68   4'25\"   68  4'20\"   68  \n",
       "11   NaN   66  29   66   4'30\"   66  4'25\"   66  \n",
       "12   8.0   64  27   64   4'35\"   64  4'30\"   64  \n",
       "13   NaN   62  25   62   4'40\"   62  4'35\"   62  \n",
       "14   7.0   60  23   60   4'45\"   60  4'40\"   60  \n",
       "15   6.0   50  21   50   5'05\"   50  4'50\"   50  \n",
       "16   5.0   40  19   40   5'25\"   40  5'00\"   40  \n",
       "17   4.0   30  17   30   5'45\"   30  5'10\"   30  \n",
       "18   3.0   20  15   20   6'05\"   20  5'20\"   20  \n",
       "19   2.0   10  13   10   6'25\"   10  5'30\"   10  \n",
       "\n",
       "[20 rows x 24 columns]"
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "stand=pd.read_excel('./体侧成绩评分表.xls',header = [0,1])\n",
    "stand"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "07fc2a93",
   "metadata": {},
   "source": [
    "### 4、数据类型转换\n",
    "       男1000米跑，数据类型是str，并且是4’26这种形式，需要变成float类型的值"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "d0ce3709",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>班级</th>\n",
       "      <th>性别</th>\n",
       "      <th>男1000米跑</th>\n",
       "      <th>男50米跑</th>\n",
       "      <th>男跳远</th>\n",
       "      <th>男体前屈</th>\n",
       "      <th>男引体</th>\n",
       "      <th>男肺活量</th>\n",
       "      <th>身高</th>\n",
       "      <th>体重</th>\n",
       "      <th>BMI</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
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       "      <th>0</th>\n",
       "      <td>1</td>\n",
       "      <td>男</td>\n",
       "      <td>4.13</td>\n",
       "      <td>8.88</td>\n",
       "      <td>195.0</td>\n",
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       "      <td>72.6</td>\n",
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       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1</td>\n",
       "      <td>男</td>\n",
       "      <td>4.16</td>\n",
       "      <td>7.70</td>\n",
       "      <td>225.0</td>\n",
       "      <td>11</td>\n",
       "      <td>7</td>\n",
       "      <td>3133</td>\n",
       "      <td>174.0</td>\n",
       "      <td>52.7</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>1</td>\n",
       "      <td>男</td>\n",
       "      <td>4.09</td>\n",
       "      <td>8.45</td>\n",
       "      <td>218.0</td>\n",
       "      <td>14</td>\n",
       "      <td>1</td>\n",
       "      <td>3901</td>\n",
       "      <td>169.0</td>\n",
       "      <td>46.5</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>1</td>\n",
       "      <td>男</td>\n",
       "      <td>4.21</td>\n",
       "      <td>8.05</td>\n",
       "      <td>206.0</td>\n",
       "      <td>13</td>\n",
       "      <td>1</td>\n",
       "      <td>4946</td>\n",
       "      <td>183.0</td>\n",
       "      <td>79.7</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>1</td>\n",
       "      <td>男</td>\n",
       "      <td>3.44</td>\n",
       "      <td>7.52</td>\n",
       "      <td>210.0</td>\n",
       "      <td>13</td>\n",
       "      <td>9</td>\n",
       "      <td>3538</td>\n",
       "      <td>171.0</td>\n",
       "      <td>54.7</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>472</th>\n",
       "      <td>17</td>\n",
       "      <td>男</td>\n",
       "      <td>4.23</td>\n",
       "      <td>8.27</td>\n",
       "      <td>208.0</td>\n",
       "      <td>10</td>\n",
       "      <td>0</td>\n",
       "      <td>4647</td>\n",
       "      <td>176.0</td>\n",
       "      <td>69.5</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>473</th>\n",
       "      <td>17</td>\n",
       "      <td>男</td>\n",
       "      <td>5.19</td>\n",
       "      <td>9.55</td>\n",
       "      <td>210.0</td>\n",
       "      <td>15</td>\n",
       "      <td>6</td>\n",
       "      <td>7042</td>\n",
       "      <td>177.0</td>\n",
       "      <td>76.0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>474</th>\n",
       "      <td>17</td>\n",
       "      <td>男</td>\n",
       "      <td>3.25</td>\n",
       "      <td>7.50</td>\n",
       "      <td>252.0</td>\n",
       "      <td>13</td>\n",
       "      <td>13</td>\n",
       "      <td>5755</td>\n",
       "      <td>181.0</td>\n",
       "      <td>65.0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>475</th>\n",
       "      <td>17</td>\n",
       "      <td>男</td>\n",
       "      <td>4.39</td>\n",
       "      <td>7.81</td>\n",
       "      <td>208.0</td>\n",
       "      <td>14</td>\n",
       "      <td>11</td>\n",
       "      <td>5688</td>\n",
       "      <td>172.0</td>\n",
       "      <td>51.7</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>476</th>\n",
       "      <td>17</td>\n",
       "      <td>男</td>\n",
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       "      <td>0.0</td>\n",
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       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>477 rows × 11 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "     班级 性别  男1000米跑  男50米跑    男跳远  男体前屈  男引体  男肺活量     身高    体重  BMI\n",
       "0     1  男     4.13   8.88  195.0    12    1  2785  170.0  72.6    0\n",
       "1     1  男     4.16   7.70  225.0    11    7  3133  174.0  52.7    0\n",
       "2     1  男     4.09   8.45  218.0    14    1  3901  169.0  46.5    0\n",
       "3     1  男     4.21   8.05  206.0    13    1  4946  183.0  79.7    0\n",
       "4     1  男     3.44   7.52  210.0    13    9  3538  171.0  54.7    0\n",
       "..   .. ..      ...    ...    ...   ...  ...   ...    ...   ...  ...\n",
       "472  17  男     4.23   8.27  208.0    10    0  4647  176.0  69.5    0\n",
       "473  17  男     5.19   9.55  210.0    15    6  7042  177.0  76.0    0\n",
       "474  17  男     3.25   7.50  252.0    13   13  5755  181.0  65.0    0\n",
       "475  17  男     4.39   7.81  208.0    14   11  5688  172.0  51.7    0\n",
       "476  17  男      NaN   0.00    0.0     0    0     0    0.0   0.0    0\n",
       "\n",
       "[477 rows x 11 columns]"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "boy['男1000米跑']=boy['男1000米跑'].str.replace(\"'\",\".\")\n",
    "boy['男1000米跑']=boy['男1000米跑'].astype(float)\n",
    "boy"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "38345e2f",
   "metadata": {},
   "source": [
    "### 评分标准中男1000米跑和女800米跑的成绩都是4‘10’‘这种形式，需要转化为float类型值"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "4165d60a",
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "  <thead>\n",
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       "      <th></th>\n",
       "      <th colspan=\"2\" halign=\"left\">男肺活量</th>\n",
       "      <th colspan=\"2\" halign=\"left\">女肺活量</th>\n",
       "      <th colspan=\"2\" halign=\"left\">男50米跑</th>\n",
       "      <th colspan=\"2\" halign=\"left\">女50米跑</th>\n",
       "      <th colspan=\"2\" halign=\"left\">男体前屈</th>\n",
       "      <th>...</th>\n",
       "      <th colspan=\"2\" halign=\"left\">女跳远</th>\n",
       "      <th colspan=\"2\" halign=\"left\">男引体</th>\n",
       "      <th colspan=\"2\" halign=\"left\">女仰卧</th>\n",
       "      <th colspan=\"2\" halign=\"left\">男1000米跑</th>\n",
       "      <th colspan=\"2\" halign=\"left\">女800米跑</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th></th>\n",
       "      <th>成绩</th>\n",
       "      <th>分数</th>\n",
       "      <th>成绩</th>\n",
       "      <th>分数</th>\n",
       "      <th>成绩</th>\n",
       "      <th>分数</th>\n",
       "      <th>成绩</th>\n",
       "      <th>分数</th>\n",
       "      <th>成绩</th>\n",
       "      <th>分数</th>\n",
       "      <th>...</th>\n",
       "      <th>成绩</th>\n",
       "      <th>分数</th>\n",
       "      <th>成绩</th>\n",
       "      <th>分数</th>\n",
       "      <th>成绩</th>\n",
       "      <th>分数</th>\n",
       "      <th>成绩</th>\n",
       "      <th>分数</th>\n",
       "      <th>成绩</th>\n",
       "      <th>分数</th>\n",
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       "  <tbody>\n",
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       "      <th>0</th>\n",
       "      <td>4540.0</td>\n",
       "      <td>100.0</td>\n",
       "      <td>3150.0</td>\n",
       "      <td>100.0</td>\n",
       "      <td>7.1</td>\n",
       "      <td>100.0</td>\n",
       "      <td>7.8</td>\n",
       "      <td>100.0</td>\n",
       "      <td>23.6</td>\n",
       "      <td>100.0</td>\n",
       "      <td>...</td>\n",
       "      <td>204.0</td>\n",
       "      <td>100.0</td>\n",
       "      <td>16.0</td>\n",
       "      <td>100.0</td>\n",
       "      <td>53.0</td>\n",
       "      <td>100.0</td>\n",
       "      <td>3.30</td>\n",
       "      <td>100.0</td>\n",
       "      <td>3.24</td>\n",
       "      <td>100.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>4420.0</td>\n",
       "      <td>95.0</td>\n",
       "      <td>3100.0</td>\n",
       "      <td>95.0</td>\n",
       "      <td>7.2</td>\n",
       "      <td>95.0</td>\n",
       "      <td>7.9</td>\n",
       "      <td>95.0</td>\n",
       "      <td>21.5</td>\n",
       "      <td>95.0</td>\n",
       "      <td>...</td>\n",
       "      <td>198.0</td>\n",
       "      <td>95.0</td>\n",
       "      <td>15.0</td>\n",
       "      <td>95.0</td>\n",
       "      <td>51.0</td>\n",
       "      <td>95.0</td>\n",
       "      <td>3.35</td>\n",
       "      <td>95.0</td>\n",
       "      <td>3.30</td>\n",
       "      <td>95.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>4300.0</td>\n",
       "      <td>90.0</td>\n",
       "      <td>3050.0</td>\n",
       "      <td>90.0</td>\n",
       "      <td>7.3</td>\n",
       "      <td>90.0</td>\n",
       "      <td>8.0</td>\n",
       "      <td>90.0</td>\n",
       "      <td>19.4</td>\n",
       "      <td>90.0</td>\n",
       "      <td>...</td>\n",
       "      <td>192.0</td>\n",
       "      <td>90.0</td>\n",
       "      <td>14.0</td>\n",
       "      <td>90.0</td>\n",
       "      <td>49.0</td>\n",
       "      <td>90.0</td>\n",
       "      <td>3.40</td>\n",
       "      <td>90.0</td>\n",
       "      <td>3.36</td>\n",
       "      <td>90.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>4050.0</td>\n",
       "      <td>85.0</td>\n",
       "      <td>2900.0</td>\n",
       "      <td>85.0</td>\n",
       "      <td>7.4</td>\n",
       "      <td>85.0</td>\n",
       "      <td>8.3</td>\n",
       "      <td>85.0</td>\n",
       "      <td>17.2</td>\n",
       "      <td>85.0</td>\n",
       "      <td>...</td>\n",
       "      <td>185.0</td>\n",
       "      <td>85.0</td>\n",
       "      <td>13.0</td>\n",
       "      <td>85.0</td>\n",
       "      <td>46.0</td>\n",
       "      <td>85.0</td>\n",
       "      <td>3.47</td>\n",
       "      <td>85.0</td>\n",
       "      <td>3.43</td>\n",
       "      <td>85.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>3800.0</td>\n",
       "      <td>80.0</td>\n",
       "      <td>2750.0</td>\n",
       "      <td>80.0</td>\n",
       "      <td>7.5</td>\n",
       "      <td>80.0</td>\n",
       "      <td>8.6</td>\n",
       "      <td>80.0</td>\n",
       "      <td>15.0</td>\n",
       "      <td>80.0</td>\n",
       "      <td>...</td>\n",
       "      <td>178.0</td>\n",
       "      <td>80.0</td>\n",
       "      <td>12.0</td>\n",
       "      <td>80.0</td>\n",
       "      <td>43.0</td>\n",
       "      <td>80.0</td>\n",
       "      <td>3.55</td>\n",
       "      <td>80.0</td>\n",
       "      <td>3.50</td>\n",
       "      <td>80.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>3680.0</td>\n",
       "      <td>78.0</td>\n",
       "      <td>2650.0</td>\n",
       "      <td>78.0</td>\n",
       "      <td>7.7</td>\n",
       "      <td>78.0</td>\n",
       "      <td>8.8</td>\n",
       "      <td>78.0</td>\n",
       "      <td>13.6</td>\n",
       "      <td>78.0</td>\n",
       "      <td>...</td>\n",
       "      <td>175.0</td>\n",
       "      <td>78.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>78.0</td>\n",
       "      <td>41.0</td>\n",
       "      <td>78.0</td>\n",
       "      <td>4.00</td>\n",
       "      <td>78.0</td>\n",
       "      <td>3.55</td>\n",
       "      <td>78.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>3560.0</td>\n",
       "      <td>76.0</td>\n",
       "      <td>2550.0</td>\n",
       "      <td>76.0</td>\n",
       "      <td>7.9</td>\n",
       "      <td>76.0</td>\n",
       "      <td>9.0</td>\n",
       "      <td>76.0</td>\n",
       "      <td>12.2</td>\n",
       "      <td>76.0</td>\n",
       "      <td>...</td>\n",
       "      <td>172.0</td>\n",
       "      <td>76.0</td>\n",
       "      <td>11.0</td>\n",
       "      <td>76.0</td>\n",
       "      <td>39.0</td>\n",
       "      <td>76.0</td>\n",
       "      <td>4.05</td>\n",
       "      <td>76.0</td>\n",
       "      <td>4.00</td>\n",
       "      <td>76.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>3440.0</td>\n",
       "      <td>74.0</td>\n",
       "      <td>2450.0</td>\n",
       "      <td>74.0</td>\n",
       "      <td>8.1</td>\n",
       "      <td>74.0</td>\n",
       "      <td>9.2</td>\n",
       "      <td>74.0</td>\n",
       "      <td>10.8</td>\n",
       "      <td>74.0</td>\n",
       "      <td>...</td>\n",
       "      <td>169.0</td>\n",
       "      <td>74.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>74.0</td>\n",
       "      <td>37.0</td>\n",
       "      <td>74.0</td>\n",
       "      <td>4.10</td>\n",
       "      <td>74.0</td>\n",
       "      <td>4.05</td>\n",
       "      <td>74.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>3320.0</td>\n",
       "      <td>72.0</td>\n",
       "      <td>2350.0</td>\n",
       "      <td>72.0</td>\n",
       "      <td>8.3</td>\n",
       "      <td>72.0</td>\n",
       "      <td>9.4</td>\n",
       "      <td>72.0</td>\n",
       "      <td>9.4</td>\n",
       "      <td>72.0</td>\n",
       "      <td>...</td>\n",
       "      <td>166.0</td>\n",
       "      <td>72.0</td>\n",
       "      <td>10.0</td>\n",
       "      <td>72.0</td>\n",
       "      <td>35.0</td>\n",
       "      <td>72.0</td>\n",
       "      <td>4.15</td>\n",
       "      <td>72.0</td>\n",
       "      <td>4.10</td>\n",
       "      <td>72.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>3200.0</td>\n",
       "      <td>70.0</td>\n",
       "      <td>2250.0</td>\n",
       "      <td>70.0</td>\n",
       "      <td>8.5</td>\n",
       "      <td>70.0</td>\n",
       "      <td>9.6</td>\n",
       "      <td>70.0</td>\n",
       "      <td>8.0</td>\n",
       "      <td>70.0</td>\n",
       "      <td>...</td>\n",
       "      <td>163.0</td>\n",
       "      <td>70.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>70.0</td>\n",
       "      <td>33.0</td>\n",
       "      <td>70.0</td>\n",
       "      <td>4.20</td>\n",
       "      <td>70.0</td>\n",
       "      <td>4.15</td>\n",
       "      <td>70.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>3080.0</td>\n",
       "      <td>68.0</td>\n",
       "      <td>2150.0</td>\n",
       "      <td>68.0</td>\n",
       "      <td>8.7</td>\n",
       "      <td>68.0</td>\n",
       "      <td>9.8</td>\n",
       "      <td>68.0</td>\n",
       "      <td>6.6</td>\n",
       "      <td>68.0</td>\n",
       "      <td>...</td>\n",
       "      <td>160.0</td>\n",
       "      <td>68.0</td>\n",
       "      <td>9.0</td>\n",
       "      <td>68.0</td>\n",
       "      <td>31.0</td>\n",
       "      <td>68.0</td>\n",
       "      <td>4.25</td>\n",
       "      <td>68.0</td>\n",
       "      <td>4.20</td>\n",
       "      <td>68.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>2960.0</td>\n",
       "      <td>66.0</td>\n",
       "      <td>2050.0</td>\n",
       "      <td>66.0</td>\n",
       "      <td>8.9</td>\n",
       "      <td>66.0</td>\n",
       "      <td>10.0</td>\n",
       "      <td>66.0</td>\n",
       "      <td>5.2</td>\n",
       "      <td>66.0</td>\n",
       "      <td>...</td>\n",
       "      <td>157.0</td>\n",
       "      <td>66.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>66.0</td>\n",
       "      <td>29.0</td>\n",
       "      <td>66.0</td>\n",
       "      <td>4.30</td>\n",
       "      <td>66.0</td>\n",
       "      <td>4.25</td>\n",
       "      <td>66.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>2840.0</td>\n",
       "      <td>64.0</td>\n",
       "      <td>1950.0</td>\n",
       "      <td>64.0</td>\n",
       "      <td>9.1</td>\n",
       "      <td>64.0</td>\n",
       "      <td>10.2</td>\n",
       "      <td>64.0</td>\n",
       "      <td>3.8</td>\n",
       "      <td>64.0</td>\n",
       "      <td>...</td>\n",
       "      <td>154.0</td>\n",
       "      <td>64.0</td>\n",
       "      <td>8.0</td>\n",
       "      <td>64.0</td>\n",
       "      <td>27.0</td>\n",
       "      <td>64.0</td>\n",
       "      <td>4.35</td>\n",
       "      <td>64.0</td>\n",
       "      <td>4.30</td>\n",
       "      <td>64.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13</th>\n",
       "      <td>2720.0</td>\n",
       "      <td>62.0</td>\n",
       "      <td>1850.0</td>\n",
       "      <td>62.0</td>\n",
       "      <td>9.3</td>\n",
       "      <td>62.0</td>\n",
       "      <td>10.4</td>\n",
       "      <td>62.0</td>\n",
       "      <td>2.4</td>\n",
       "      <td>62.0</td>\n",
       "      <td>...</td>\n",
       "      <td>151.0</td>\n",
       "      <td>62.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>62.0</td>\n",
       "      <td>25.0</td>\n",
       "      <td>62.0</td>\n",
       "      <td>4.40</td>\n",
       "      <td>62.0</td>\n",
       "      <td>4.35</td>\n",
       "      <td>62.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14</th>\n",
       "      <td>2600.0</td>\n",
       "      <td>60.0</td>\n",
       "      <td>1750.0</td>\n",
       "      <td>60.0</td>\n",
       "      <td>9.5</td>\n",
       "      <td>60.0</td>\n",
       "      <td>10.6</td>\n",
       "      <td>60.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>60.0</td>\n",
       "      <td>...</td>\n",
       "      <td>148.0</td>\n",
       "      <td>60.0</td>\n",
       "      <td>7.0</td>\n",
       "      <td>60.0</td>\n",
       "      <td>23.0</td>\n",
       "      <td>60.0</td>\n",
       "      <td>4.45</td>\n",
       "      <td>60.0</td>\n",
       "      <td>4.40</td>\n",
       "      <td>60.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15</th>\n",
       "      <td>2470.0</td>\n",
       "      <td>50.0</td>\n",
       "      <td>1710.0</td>\n",
       "      <td>50.0</td>\n",
       "      <td>9.7</td>\n",
       "      <td>50.0</td>\n",
       "      <td>10.8</td>\n",
       "      <td>50.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>50.0</td>\n",
       "      <td>...</td>\n",
       "      <td>143.0</td>\n",
       "      <td>50.0</td>\n",
       "      <td>6.0</td>\n",
       "      <td>50.0</td>\n",
       "      <td>21.0</td>\n",
       "      <td>50.0</td>\n",
       "      <td>5.05</td>\n",
       "      <td>50.0</td>\n",
       "      <td>4.50</td>\n",
       "      <td>50.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16</th>\n",
       "      <td>2340.0</td>\n",
       "      <td>40.0</td>\n",
       "      <td>1670.0</td>\n",
       "      <td>40.0</td>\n",
       "      <td>9.9</td>\n",
       "      <td>40.0</td>\n",
       "      <td>11.0</td>\n",
       "      <td>40.0</td>\n",
       "      <td>-1.0</td>\n",
       "      <td>40.0</td>\n",
       "      <td>...</td>\n",
       "      <td>138.0</td>\n",
       "      <td>40.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>40.0</td>\n",
       "      <td>19.0</td>\n",
       "      <td>40.0</td>\n",
       "      <td>5.25</td>\n",
       "      <td>40.0</td>\n",
       "      <td>5.00</td>\n",
       "      <td>40.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>17</th>\n",
       "      <td>2210.0</td>\n",
       "      <td>30.0</td>\n",
       "      <td>1630.0</td>\n",
       "      <td>30.0</td>\n",
       "      <td>10.1</td>\n",
       "      <td>30.0</td>\n",
       "      <td>11.2</td>\n",
       "      <td>30.0</td>\n",
       "      <td>-2.0</td>\n",
       "      <td>30.0</td>\n",
       "      <td>...</td>\n",
       "      <td>133.0</td>\n",
       "      <td>30.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>30.0</td>\n",
       "      <td>17.0</td>\n",
       "      <td>30.0</td>\n",
       "      <td>5.45</td>\n",
       "      <td>30.0</td>\n",
       "      <td>5.10</td>\n",
       "      <td>30.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>18</th>\n",
       "      <td>2080.0</td>\n",
       "      <td>20.0</td>\n",
       "      <td>1590.0</td>\n",
       "      <td>20.0</td>\n",
       "      <td>10.3</td>\n",
       "      <td>20.0</td>\n",
       "      <td>11.4</td>\n",
       "      <td>20.0</td>\n",
       "      <td>-3.0</td>\n",
       "      <td>20.0</td>\n",
       "      <td>...</td>\n",
       "      <td>128.0</td>\n",
       "      <td>20.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>20.0</td>\n",
       "      <td>15.0</td>\n",
       "      <td>20.0</td>\n",
       "      <td>6.05</td>\n",
       "      <td>20.0</td>\n",
       "      <td>5.20</td>\n",
       "      <td>20.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>19</th>\n",
       "      <td>1950.0</td>\n",
       "      <td>10.0</td>\n",
       "      <td>1550.0</td>\n",
       "      <td>10.0</td>\n",
       "      <td>10.5</td>\n",
       "      <td>10.0</td>\n",
       "      <td>11.6</td>\n",
       "      <td>10.0</td>\n",
       "      <td>-4.0</td>\n",
       "      <td>10.0</td>\n",
       "      <td>...</td>\n",
       "      <td>123.0</td>\n",
       "      <td>10.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>10.0</td>\n",
       "      <td>13.0</td>\n",
       "      <td>10.0</td>\n",
       "      <td>6.25</td>\n",
       "      <td>10.0</td>\n",
       "      <td>5.30</td>\n",
       "      <td>10.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>20 rows × 24 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "      男肺活量           女肺活量        男50米跑        女50米跑         男体前屈         ...  \\\n",
       "        成绩     分数      成绩     分数    成绩     分数    成绩     分数    成绩     分数  ...   \n",
       "0   4540.0  100.0  3150.0  100.0   7.1  100.0   7.8  100.0  23.6  100.0  ...   \n",
       "1   4420.0   95.0  3100.0   95.0   7.2   95.0   7.9   95.0  21.5   95.0  ...   \n",
       "2   4300.0   90.0  3050.0   90.0   7.3   90.0   8.0   90.0  19.4   90.0  ...   \n",
       "3   4050.0   85.0  2900.0   85.0   7.4   85.0   8.3   85.0  17.2   85.0  ...   \n",
       "4   3800.0   80.0  2750.0   80.0   7.5   80.0   8.6   80.0  15.0   80.0  ...   \n",
       "5   3680.0   78.0  2650.0   78.0   7.7   78.0   8.8   78.0  13.6   78.0  ...   \n",
       "6   3560.0   76.0  2550.0   76.0   7.9   76.0   9.0   76.0  12.2   76.0  ...   \n",
       "7   3440.0   74.0  2450.0   74.0   8.1   74.0   9.2   74.0  10.8   74.0  ...   \n",
       "8   3320.0   72.0  2350.0   72.0   8.3   72.0   9.4   72.0   9.4   72.0  ...   \n",
       "9   3200.0   70.0  2250.0   70.0   8.5   70.0   9.6   70.0   8.0   70.0  ...   \n",
       "10  3080.0   68.0  2150.0   68.0   8.7   68.0   9.8   68.0   6.6   68.0  ...   \n",
       "11  2960.0   66.0  2050.0   66.0   8.9   66.0  10.0   66.0   5.2   66.0  ...   \n",
       "12  2840.0   64.0  1950.0   64.0   9.1   64.0  10.2   64.0   3.8   64.0  ...   \n",
       "13  2720.0   62.0  1850.0   62.0   9.3   62.0  10.4   62.0   2.4   62.0  ...   \n",
       "14  2600.0   60.0  1750.0   60.0   9.5   60.0  10.6   60.0   1.0   60.0  ...   \n",
       "15  2470.0   50.0  1710.0   50.0   9.7   50.0  10.8   50.0   0.0   50.0  ...   \n",
       "16  2340.0   40.0  1670.0   40.0   9.9   40.0  11.0   40.0  -1.0   40.0  ...   \n",
       "17  2210.0   30.0  1630.0   30.0  10.1   30.0  11.2   30.0  -2.0   30.0  ...   \n",
       "18  2080.0   20.0  1590.0   20.0  10.3   20.0  11.4   20.0  -3.0   20.0  ...   \n",
       "19  1950.0   10.0  1550.0   10.0  10.5   10.0  11.6   10.0  -4.0   10.0  ...   \n",
       "\n",
       "      女跳远          男引体          女仰卧        男1000米跑        女800米跑         \n",
       "       成绩     分数    成绩     分数    成绩     分数      成绩     分数     成绩     分数  \n",
       "0   204.0  100.0  16.0  100.0  53.0  100.0    3.30  100.0   3.24  100.0  \n",
       "1   198.0   95.0  15.0   95.0  51.0   95.0    3.35   95.0   3.30   95.0  \n",
       "2   192.0   90.0  14.0   90.0  49.0   90.0    3.40   90.0   3.36   90.0  \n",
       "3   185.0   85.0  13.0   85.0  46.0   85.0    3.47   85.0   3.43   85.0  \n",
       "4   178.0   80.0  12.0   80.0  43.0   80.0    3.55   80.0   3.50   80.0  \n",
       "5   175.0   78.0   NaN   78.0  41.0   78.0    4.00   78.0   3.55   78.0  \n",
       "6   172.0   76.0  11.0   76.0  39.0   76.0    4.05   76.0   4.00   76.0  \n",
       "7   169.0   74.0   NaN   74.0  37.0   74.0    4.10   74.0   4.05   74.0  \n",
       "8   166.0   72.0  10.0   72.0  35.0   72.0    4.15   72.0   4.10   72.0  \n",
       "9   163.0   70.0   NaN   70.0  33.0   70.0    4.20   70.0   4.15   70.0  \n",
       "10  160.0   68.0   9.0   68.0  31.0   68.0    4.25   68.0   4.20   68.0  \n",
       "11  157.0   66.0   NaN   66.0  29.0   66.0    4.30   66.0   4.25   66.0  \n",
       "12  154.0   64.0   8.0   64.0  27.0   64.0    4.35   64.0   4.30   64.0  \n",
       "13  151.0   62.0   NaN   62.0  25.0   62.0    4.40   62.0   4.35   62.0  \n",
       "14  148.0   60.0   7.0   60.0  23.0   60.0    4.45   60.0   4.40   60.0  \n",
       "15  143.0   50.0   6.0   50.0  21.0   50.0    5.05   50.0   4.50   50.0  \n",
       "16  138.0   40.0   5.0   40.0  19.0   40.0    5.25   40.0   5.00   40.0  \n",
       "17  133.0   30.0   4.0   30.0  17.0   30.0    5.45   30.0   5.10   30.0  \n",
       "18  128.0   20.0   3.0   20.0  15.0   20.0    6.05   20.0   5.20   20.0  \n",
       "19  123.0   10.0   2.0   10.0  13.0   10.0    6.25   10.0   5.30   10.0  \n",
       "\n",
       "[20 rows x 24 columns]"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "stand=stand.replace(\"'\",\".\",regex=True)\n",
    "stand=stand.replace('\"',\"\",regex=True)\n",
    "stand=stand.astype(float)\n",
    "stand"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "ff2a3f6e",
   "metadata": {},
   "source": [
    "### 其他所有数值类型的值，都要转换为float类型的值"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "e308b9f2",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "班级           int64\n",
      "性别          object\n",
      "男1000米跑     object\n",
      "男50米跑      float64\n",
      "男跳远        float64\n",
      "男体前屈         int64\n",
      "男引体          int64\n",
      "男肺活量         int64\n",
      "身高         float64\n",
      "体重         float64\n",
      "BMI          int64\n",
      "dtype: object 班级          int64\n",
      "性别         object\n",
      "女800米跑    float64\n",
      "女50米跑     float64\n",
      "女跳远       float64\n",
      "女体前屈        int64\n",
      "女仰卧         int64\n",
      "女肺活量        int64\n",
      "身高        float64\n",
      "体重        float64\n",
      "BMI         int64\n",
      "dtype: object 男肺活量     成绩    float64\n",
      "         分数    float64\n",
      "女肺活量     成绩    float64\n",
      "         分数    float64\n",
      "男50米跑    成绩    float64\n",
      "         分数    float64\n",
      "女50米跑    成绩    float64\n",
      "         分数    float64\n",
      "男体前屈     成绩    float64\n",
      "         分数    float64\n",
      "女体前屈     成绩    float64\n",
      "         分数    float64\n",
      "男跳远      成绩    float64\n",
      "         分数    float64\n",
      "女跳远      成绩    float64\n",
      "         分数    float64\n",
      "男引体      成绩    float64\n",
      "         分数    float64\n",
      "女仰卧      成绩    float64\n",
      "         分数    float64\n",
      "男1000米跑  成绩    float64\n",
      "         分数    float64\n",
      "女800米跑   成绩    float64\n",
      "         分数    float64\n",
      "dtype: object\n"
     ]
    }
   ],
   "source": [
    "print(boy.dtypes,girl.dtypes,stand.dtypes)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "1e83f04e",
   "metadata": {},
   "outputs": [],
   "source": [
    "boy[['男体前屈','男引体','男肺活量','BMI']]=boy[['男体前屈','男引体','男肺活量','BMI']].astype(float)\n",
    "girl[['女体前屈','女仰卧','女肺活量','BMI']]=girl[['女体前屈','女仰卧','女肺活量','BMI']].astype(float)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "id": "fc5293da",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "班级           int64\n",
      "性别          object\n",
      "男1000米跑    float64\n",
      "男50米跑      float64\n",
      "男跳远        float64\n",
      "男体前屈       float64\n",
      "男引体        float64\n",
      "男肺活量       float64\n",
      "身高         float64\n",
      "体重         float64\n",
      "BMI        float64\n",
      "dtype: object 班级          int64\n",
      "性别         object\n",
      "女800米跑    float64\n",
      "女50米跑     float64\n",
      "女跳远       float64\n",
      "女体前屈      float64\n",
      "女仰卧       float64\n",
      "女肺活量      float64\n",
      "身高        float64\n",
      "体重        float64\n",
      "BMI       float64\n",
      "dtype: object 男肺活量     成绩    float64\n",
      "         分数    float64\n",
      "女肺活量     成绩    float64\n",
      "         分数    float64\n",
      "男50米跑    成绩    float64\n",
      "         分数    float64\n",
      "女50米跑    成绩    float64\n",
      "         分数    float64\n",
      "男体前屈     成绩    float64\n",
      "         分数    float64\n",
      "女体前屈     成绩    float64\n",
      "         分数    float64\n",
      "男跳远      成绩    float64\n",
      "         分数    float64\n",
      "女跳远      成绩    float64\n",
      "         分数    float64\n",
      "男引体      成绩    float64\n",
      "         分数    float64\n",
      "女仰卧      成绩    float64\n",
      "         分数    float64\n",
      "男1000米跑  成绩    float64\n",
      "         分数    float64\n",
      "女800米跑   成绩    float64\n",
      "         分数    float64\n",
      "dtype: object\n"
     ]
    }
   ],
   "source": [
    "print(boy.dtypes,girl.dtypes,stand.dtypes)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "d9d8ddaa",
   "metadata": {},
   "source": [
    "### 5、对体测成绩进行分数转换，跑步类（越小越好）；跳远、体前屈（越大越好）\n",
    "\n",
    "       使用map、apply、transform方法\n",
    "\n",
    "       列索引重排\n",
    "\n",
    "       转换之后效果"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "59a9af62",
   "metadata": {},
   "source": [
    "from pandas import Series,DataFrame"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 205,
   "id": "b10e0c89",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{\"<class 'str'>\"}"
      ]
     },
     "execution_count": 205,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "types = set()\n",
    "for boy_1000 in stand['男1000米跑']:\n",
    "    types.add(str(type(man_1000)))\n",
    "types"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "id": "2f48cf9f",
   "metadata": {},
   "outputs": [],
   "source": [
    "\n",
    "def convert(x):\n",
    "    if isinstance(x,str):\n",
    "        minute,second=x.split(\"'\")\n",
    "        minute=int(minute)\n",
    "        second=int(second)\n",
    "        return minute + second/60.0\n",
    "    else:\n",
    "        return x\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "id": "66592bfb",
   "metadata": {},
   "outputs": [
    {
     "ename": "AttributeError",
     "evalue": "'DataFrame' object has no attribute 'map'",
     "output_type": "error",
     "traceback": [
      "\u001b[1;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[1;31mAttributeError\u001b[0m                            Traceback (most recent call last)",
      "\u001b[1;32m~\\AppData\\Local\\Temp\\ipykernel_17304\\352028627.py\u001b[0m in \u001b[0;36m<module>\u001b[1;34m\u001b[0m\n\u001b[1;32m----> 1\u001b[1;33m \u001b[0mstand\u001b[0m\u001b[1;33m[\u001b[0m\u001b[1;34m'boy_1000'\u001b[0m\u001b[1;33m]\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mstand\u001b[0m\u001b[1;33m[\u001b[0m\u001b[1;34m'男1000米跑'\u001b[0m\u001b[1;33m]\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mmap\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mconvert\u001b[0m\u001b[1;33m)\u001b[0m   \u001b[1;31m# 映射\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m      2\u001b[0m \u001b[0mstand\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mhead\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;32mc:\\users\\hp 14\\appdata\\local\\programs\\python\\python37\\lib\\site-packages\\pandas\\core\\generic.py\u001b[0m in \u001b[0;36m__getattr__\u001b[1;34m(self, name)\u001b[0m\n\u001b[0;32m   5485\u001b[0m         ):\n\u001b[0;32m   5486\u001b[0m             \u001b[1;32mreturn\u001b[0m \u001b[0mself\u001b[0m\u001b[1;33m[\u001b[0m\u001b[0mname\u001b[0m\u001b[1;33m]\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m-> 5487\u001b[1;33m         \u001b[1;32mreturn\u001b[0m \u001b[0mobject\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0m__getattribute__\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mself\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mname\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m   5488\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m   5489\u001b[0m     \u001b[1;32mdef\u001b[0m \u001b[0m__setattr__\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mself\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mname\u001b[0m\u001b[1;33m:\u001b[0m \u001b[0mstr\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mvalue\u001b[0m\u001b[1;33m)\u001b[0m \u001b[1;33m->\u001b[0m \u001b[1;32mNone\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;31mAttributeError\u001b[0m: 'DataFrame' object has no attribute 'map'"
     ]
    }
   ],
   "source": [
    "stand['boy_1000'] = stand['男1000米跑'].map(convert)   # 映射\n",
    "stand.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "77153009",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "id": "081c373f",
   "metadata": {},
   "outputs": [],
   "source": [
    "def convert2(d):\n",
    "    m,n = d[0:-1].split(\"'\")\n",
    "    m,n = int(m),int(n)\n",
    "    return m + n/60"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "id": "e4749bb4",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead tr th {\n",
       "        text-align: left;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr>\n",
       "      <th></th>\n",
       "      <th colspan=\"2\" halign=\"left\">男肺活量</th>\n",
       "      <th colspan=\"2\" halign=\"left\">女肺活量</th>\n",
       "      <th colspan=\"2\" halign=\"left\">男50米跑</th>\n",
       "      <th colspan=\"2\" halign=\"left\">女50米跑</th>\n",
       "      <th colspan=\"2\" halign=\"left\">男体前屈</th>\n",
       "      <th>...</th>\n",
       "      <th colspan=\"2\" halign=\"left\">女跳远</th>\n",
       "      <th colspan=\"2\" halign=\"left\">男引体</th>\n",
       "      <th colspan=\"2\" halign=\"left\">女仰卧</th>\n",
       "      <th colspan=\"2\" halign=\"left\">男1000米跑</th>\n",
       "      <th colspan=\"2\" halign=\"left\">女800米跑</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th></th>\n",
       "      <th>成绩</th>\n",
       "      <th>分数</th>\n",
       "      <th>成绩</th>\n",
       "      <th>分数</th>\n",
       "      <th>成绩</th>\n",
       "      <th>分数</th>\n",
       "      <th>成绩</th>\n",
       "      <th>分数</th>\n",
       "      <th>成绩</th>\n",
       "      <th>分数</th>\n",
       "      <th>...</th>\n",
       "      <th>成绩</th>\n",
       "      <th>分数</th>\n",
       "      <th>成绩</th>\n",
       "      <th>分数</th>\n",
       "      <th>成绩</th>\n",
       "      <th>分数</th>\n",
       "      <th>成绩</th>\n",
       "      <th>分数</th>\n",
       "      <th>成绩</th>\n",
       "      <th>分数</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>4540.0</td>\n",
       "      <td>100.0</td>\n",
       "      <td>3150.0</td>\n",
       "      <td>100.0</td>\n",
       "      <td>7.1</td>\n",
       "      <td>100.0</td>\n",
       "      <td>7.8</td>\n",
       "      <td>100.0</td>\n",
       "      <td>23.6</td>\n",
       "      <td>100.0</td>\n",
       "      <td>...</td>\n",
       "      <td>204.0</td>\n",
       "      <td>100.0</td>\n",
       "      <td>16.0</td>\n",
       "      <td>100.0</td>\n",
       "      <td>53.0</td>\n",
       "      <td>100.0</td>\n",
       "      <td>3.30</td>\n",
       "      <td>100.0</td>\n",
       "      <td>3.24</td>\n",
       "      <td>100.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>4420.0</td>\n",
       "      <td>95.0</td>\n",
       "      <td>3100.0</td>\n",
       "      <td>95.0</td>\n",
       "      <td>7.2</td>\n",
       "      <td>95.0</td>\n",
       "      <td>7.9</td>\n",
       "      <td>95.0</td>\n",
       "      <td>21.5</td>\n",
       "      <td>95.0</td>\n",
       "      <td>...</td>\n",
       "      <td>198.0</td>\n",
       "      <td>95.0</td>\n",
       "      <td>15.0</td>\n",
       "      <td>95.0</td>\n",
       "      <td>51.0</td>\n",
       "      <td>95.0</td>\n",
       "      <td>3.35</td>\n",
       "      <td>95.0</td>\n",
       "      <td>3.30</td>\n",
       "      <td>95.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>2 rows × 24 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "     男肺活量           女肺活量        男50米跑        女50米跑         男体前屈         ...  \\\n",
       "       成绩     分数      成绩     分数    成绩     分数    成绩     分数    成绩     分数  ...   \n",
       "0  4540.0  100.0  3150.0  100.0   7.1  100.0   7.8  100.0  23.6  100.0  ...   \n",
       "1  4420.0   95.0  3100.0   95.0   7.2   95.0   7.9   95.0  21.5   95.0  ...   \n",
       "\n",
       "     女跳远          男引体          女仰卧        男1000米跑        女800米跑         \n",
       "      成绩     分数    成绩     分数    成绩     分数      成绩     分数     成绩     分数  \n",
       "0  204.0  100.0  16.0  100.0  53.0  100.0    3.30  100.0   3.24  100.0  \n",
       "1  198.0   95.0  15.0   95.0  51.0   95.0    3.35   95.0   3.30   95.0  \n",
       "\n",
       "[2 rows x 24 columns]"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "stand.head(2)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "id": "9d7c830e",
   "metadata": {},
   "outputs": [
    {
     "ename": "TypeError",
     "evalue": "'float' object is not subscriptable",
     "output_type": "error",
     "traceback": [
      "\u001b[1;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[1;31mTypeError\u001b[0m                                 Traceback (most recent call last)",
      "\u001b[1;32m~\\AppData\\Local\\Temp\\ipykernel_9312\\2925753923.py\u001b[0m in \u001b[0;36m<module>\u001b[1;34m\u001b[0m\n\u001b[1;32m----> 1\u001b[1;33m \u001b[0mstand\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0miloc\u001b[0m\u001b[1;33m[\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m,\u001b[0m\u001b[1;33m-\u001b[0m\u001b[1;36m4\u001b[0m\u001b[1;33m]\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mstand\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0miloc\u001b[0m\u001b[1;33m[\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m,\u001b[0m\u001b[1;33m-\u001b[0m\u001b[1;36m4\u001b[0m\u001b[1;33m]\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mapply\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mconvert2\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m      2\u001b[0m \u001b[0mstand\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mhead\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;32mc:\\users\\hp 14\\appdata\\local\\programs\\python\\python37\\lib\\site-packages\\pandas\\core\\series.py\u001b[0m in \u001b[0;36mapply\u001b[1;34m(self, func, convert_dtype, args, **kwargs)\u001b[0m\n\u001b[0;32m   4355\u001b[0m         \u001b[0mdtype\u001b[0m\u001b[1;33m:\u001b[0m \u001b[0mfloat64\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m   4356\u001b[0m         \"\"\"\n\u001b[1;32m-> 4357\u001b[1;33m         \u001b[1;32mreturn\u001b[0m \u001b[0mSeriesApply\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mself\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mfunc\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mconvert_dtype\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0margs\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mkwargs\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mapply\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m   4358\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m   4359\u001b[0m     def _reduce(\n",
      "\u001b[1;32mc:\\users\\hp 14\\appdata\\local\\programs\\python\\python37\\lib\\site-packages\\pandas\\core\\apply.py\u001b[0m in \u001b[0;36mapply\u001b[1;34m(self)\u001b[0m\n\u001b[0;32m   1041\u001b[0m             \u001b[1;32mreturn\u001b[0m \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mapply_str\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m   1042\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m-> 1043\u001b[1;33m         \u001b[1;32mreturn\u001b[0m \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mapply_standard\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m   1044\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m   1045\u001b[0m     \u001b[1;32mdef\u001b[0m \u001b[0magg\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mself\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;32mc:\\users\\hp 14\\appdata\\local\\programs\\python\\python37\\lib\\site-packages\\pandas\\core\\apply.py\u001b[0m in \u001b[0;36mapply_standard\u001b[1;34m(self)\u001b[0m\n\u001b[0;32m   1099\u001b[0m                     \u001b[0mvalues\u001b[0m\u001b[1;33m,\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m   1100\u001b[0m                     \u001b[0mf\u001b[0m\u001b[1;33m,\u001b[0m  \u001b[1;31m# type: ignore[arg-type]\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m-> 1101\u001b[1;33m                     \u001b[0mconvert\u001b[0m\u001b[1;33m=\u001b[0m\u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mconvert_dtype\u001b[0m\u001b[1;33m,\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m   1102\u001b[0m                 )\n\u001b[0;32m   1103\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;32mc:\\users\\hp 14\\appdata\\local\\programs\\python\\python37\\lib\\site-packages\\pandas\\_libs\\lib.pyx\u001b[0m in \u001b[0;36mpandas._libs.lib.map_infer\u001b[1;34m()\u001b[0m\n",
      "\u001b[1;32m~\\AppData\\Local\\Temp\\ipykernel_9312\\3880170779.py\u001b[0m in \u001b[0;36mconvert2\u001b[1;34m(d)\u001b[0m\n\u001b[0;32m      1\u001b[0m \u001b[1;32mdef\u001b[0m \u001b[0mconvert2\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0md\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m----> 2\u001b[1;33m     \u001b[0mm\u001b[0m\u001b[1;33m,\u001b[0m\u001b[0mn\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0md\u001b[0m\u001b[1;33m[\u001b[0m\u001b[1;36m0\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m-\u001b[0m\u001b[1;36m1\u001b[0m\u001b[1;33m]\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0msplit\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;34m\"'\"\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m      3\u001b[0m     \u001b[0mm\u001b[0m\u001b[1;33m,\u001b[0m\u001b[0mn\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mint\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mm\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m,\u001b[0m\u001b[0mint\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mn\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m      4\u001b[0m     \u001b[1;32mreturn\u001b[0m \u001b[0mm\u001b[0m \u001b[1;33m+\u001b[0m \u001b[0mn\u001b[0m\u001b[1;33m/\u001b[0m\u001b[1;36m60\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;31mTypeError\u001b[0m: 'float' object is not subscriptable"
     ]
    }
   ],
   "source": [
    "stand.iloc[:,-4] = stand.iloc[:,-4].apply(convert2)\n",
    "stand.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "34136323",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 66,
   "id": "70d974e2",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "MultiIndex([(   '男肺活量', '成绩'),\n",
       "            (   '男肺活量', '分数'),\n",
       "            (   '女肺活量', '成绩'),\n",
       "            (   '女肺活量', '分数'),\n",
       "            (  '男50米跑', '成绩'),\n",
       "            (  '男50米跑', '分数'),\n",
       "            (  '女50米跑', '成绩'),\n",
       "            (  '女50米跑', '分数'),\n",
       "            (   '男体前屈', '成绩'),\n",
       "            (   '男体前屈', '分数'),\n",
       "            (   '女体前屈', '成绩'),\n",
       "            (   '女体前屈', '分数'),\n",
       "            (    '男跳远', '成绩'),\n",
       "            (    '男跳远', '分数'),\n",
       "            (    '女跳远', '成绩'),\n",
       "            (    '女跳远', '分数'),\n",
       "            (    '男引体', '成绩'),\n",
       "            (    '男引体', '分数'),\n",
       "            (    '女仰卧', '成绩'),\n",
       "            (    '女仰卧', '分数'),\n",
       "            ('男1000米跑', '成绩'),\n",
       "            ('男1000米跑', '分数'),\n",
       "            ( '女800米跑', '成绩'),\n",
       "            ( '女800米跑', '分数')],\n",
       "           )"
      ]
     },
     "execution_count": 66,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "stand.columns # 列索引"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 91,
   "id": "abcf3891",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead tr th {\n",
       "        text-align: left;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr>\n",
       "      <th></th>\n",
       "      <th>index</th>\n",
       "      <th colspan=\"2\" halign=\"left\">男肺活量</th>\n",
       "      <th colspan=\"2\" halign=\"left\">女肺活量</th>\n",
       "      <th colspan=\"2\" halign=\"left\">男50米跑</th>\n",
       "      <th colspan=\"2\" halign=\"left\">女50米跑</th>\n",
       "      <th>男体前屈</th>\n",
       "      <th>...</th>\n",
       "      <th colspan=\"2\" halign=\"left\">女跳远</th>\n",
       "      <th colspan=\"2\" halign=\"left\">男引体</th>\n",
       "      <th colspan=\"2\" halign=\"left\">女仰卧</th>\n",
       "      <th colspan=\"2\" halign=\"left\">男1000米跑</th>\n",
       "      <th colspan=\"2\" halign=\"left\">女800米跑</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th>成绩</th>\n",
       "      <th>分数</th>\n",
       "      <th>成绩</th>\n",
       "      <th>分数</th>\n",
       "      <th>成绩</th>\n",
       "      <th>分数</th>\n",
       "      <th>成绩</th>\n",
       "      <th>分数</th>\n",
       "      <th>成绩</th>\n",
       "      <th>...</th>\n",
       "      <th>成绩</th>\n",
       "      <th>分数</th>\n",
       "      <th>成绩</th>\n",
       "      <th>分数</th>\n",
       "      <th>成绩</th>\n",
       "      <th>分数</th>\n",
       "      <th>成绩</th>\n",
       "      <th>分数</th>\n",
       "      <th>成绩</th>\n",
       "      <th>分数</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>0</td>\n",
       "      <td>4540</td>\n",
       "      <td>100</td>\n",
       "      <td>3150</td>\n",
       "      <td>100</td>\n",
       "      <td>7.1</td>\n",
       "      <td>100</td>\n",
       "      <td>7.8</td>\n",
       "      <td>100</td>\n",
       "      <td>23.6</td>\n",
       "      <td>...</td>\n",
       "      <td>204</td>\n",
       "      <td>100</td>\n",
       "      <td>16.0</td>\n",
       "      <td>100</td>\n",
       "      <td>53</td>\n",
       "      <td>100</td>\n",
       "      <td>3'30\"</td>\n",
       "      <td>100</td>\n",
       "      <td>3'24\"</td>\n",
       "      <td>100</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1</td>\n",
       "      <td>4420</td>\n",
       "      <td>95</td>\n",
       "      <td>3100</td>\n",
       "      <td>95</td>\n",
       "      <td>7.2</td>\n",
       "      <td>95</td>\n",
       "      <td>7.9</td>\n",
       "      <td>95</td>\n",
       "      <td>21.5</td>\n",
       "      <td>...</td>\n",
       "      <td>198</td>\n",
       "      <td>95</td>\n",
       "      <td>15.0</td>\n",
       "      <td>95</td>\n",
       "      <td>51</td>\n",
       "      <td>95</td>\n",
       "      <td>3'35\"</td>\n",
       "      <td>95</td>\n",
       "      <td>3'30\"</td>\n",
       "      <td>95</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>2</td>\n",
       "      <td>4300</td>\n",
       "      <td>90</td>\n",
       "      <td>3050</td>\n",
       "      <td>90</td>\n",
       "      <td>7.3</td>\n",
       "      <td>90</td>\n",
       "      <td>8.0</td>\n",
       "      <td>90</td>\n",
       "      <td>19.4</td>\n",
       "      <td>...</td>\n",
       "      <td>192</td>\n",
       "      <td>90</td>\n",
       "      <td>14.0</td>\n",
       "      <td>90</td>\n",
       "      <td>49</td>\n",
       "      <td>90</td>\n",
       "      <td>3'40\"</td>\n",
       "      <td>90</td>\n",
       "      <td>3'36\"</td>\n",
       "      <td>90</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>3</td>\n",
       "      <td>4050</td>\n",
       "      <td>85</td>\n",
       "      <td>2900</td>\n",
       "      <td>85</td>\n",
       "      <td>7.4</td>\n",
       "      <td>85</td>\n",
       "      <td>8.3</td>\n",
       "      <td>85</td>\n",
       "      <td>17.2</td>\n",
       "      <td>...</td>\n",
       "      <td>185</td>\n",
       "      <td>85</td>\n",
       "      <td>13.0</td>\n",
       "      <td>85</td>\n",
       "      <td>46</td>\n",
       "      <td>85</td>\n",
       "      <td>3'47\"</td>\n",
       "      <td>85</td>\n",
       "      <td>3'43\"</td>\n",
       "      <td>85</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>4</td>\n",
       "      <td>3800</td>\n",
       "      <td>80</td>\n",
       "      <td>2750</td>\n",
       "      <td>80</td>\n",
       "      <td>7.5</td>\n",
       "      <td>80</td>\n",
       "      <td>8.6</td>\n",
       "      <td>80</td>\n",
       "      <td>15.0</td>\n",
       "      <td>...</td>\n",
       "      <td>178</td>\n",
       "      <td>80</td>\n",
       "      <td>12.0</td>\n",
       "      <td>80</td>\n",
       "      <td>43</td>\n",
       "      <td>80</td>\n",
       "      <td>3'55\"</td>\n",
       "      <td>80</td>\n",
       "      <td>3'50\"</td>\n",
       "      <td>80</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>5</td>\n",
       "      <td>3680</td>\n",
       "      <td>78</td>\n",
       "      <td>2650</td>\n",
       "      <td>78</td>\n",
       "      <td>7.7</td>\n",
       "      <td>78</td>\n",
       "      <td>8.8</td>\n",
       "      <td>78</td>\n",
       "      <td>13.6</td>\n",
       "      <td>...</td>\n",
       "      <td>175</td>\n",
       "      <td>78</td>\n",
       "      <td>NaN</td>\n",
       "      <td>78</td>\n",
       "      <td>41</td>\n",
       "      <td>78</td>\n",
       "      <td>4'00\"</td>\n",
       "      <td>78</td>\n",
       "      <td>3'55\"</td>\n",
       "      <td>78</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>6</td>\n",
       "      <td>3560</td>\n",
       "      <td>76</td>\n",
       "      <td>2550</td>\n",
       "      <td>76</td>\n",
       "      <td>7.9</td>\n",
       "      <td>76</td>\n",
       "      <td>9.0</td>\n",
       "      <td>76</td>\n",
       "      <td>12.2</td>\n",
       "      <td>...</td>\n",
       "      <td>172</td>\n",
       "      <td>76</td>\n",
       "      <td>11.0</td>\n",
       "      <td>76</td>\n",
       "      <td>39</td>\n",
       "      <td>76</td>\n",
       "      <td>4'05\"</td>\n",
       "      <td>76</td>\n",
       "      <td>4'00\"</td>\n",
       "      <td>76</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>7</td>\n",
       "      <td>3440</td>\n",
       "      <td>74</td>\n",
       "      <td>2450</td>\n",
       "      <td>74</td>\n",
       "      <td>8.1</td>\n",
       "      <td>74</td>\n",
       "      <td>9.2</td>\n",
       "      <td>74</td>\n",
       "      <td>10.8</td>\n",
       "      <td>...</td>\n",
       "      <td>169</td>\n",
       "      <td>74</td>\n",
       "      <td>NaN</td>\n",
       "      <td>74</td>\n",
       "      <td>37</td>\n",
       "      <td>74</td>\n",
       "      <td>4'10\"</td>\n",
       "      <td>74</td>\n",
       "      <td>4'05\"</td>\n",
       "      <td>74</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>8</td>\n",
       "      <td>3320</td>\n",
       "      <td>72</td>\n",
       "      <td>2350</td>\n",
       "      <td>72</td>\n",
       "      <td>8.3</td>\n",
       "      <td>72</td>\n",
       "      <td>9.4</td>\n",
       "      <td>72</td>\n",
       "      <td>9.4</td>\n",
       "      <td>...</td>\n",
       "      <td>166</td>\n",
       "      <td>72</td>\n",
       "      <td>10.0</td>\n",
       "      <td>72</td>\n",
       "      <td>35</td>\n",
       "      <td>72</td>\n",
       "      <td>4'15\"</td>\n",
       "      <td>72</td>\n",
       "      <td>4'10\"</td>\n",
       "      <td>72</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>9</td>\n",
       "      <td>3200</td>\n",
       "      <td>70</td>\n",
       "      <td>2250</td>\n",
       "      <td>70</td>\n",
       "      <td>8.5</td>\n",
       "      <td>70</td>\n",
       "      <td>9.6</td>\n",
       "      <td>70</td>\n",
       "      <td>8.0</td>\n",
       "      <td>...</td>\n",
       "      <td>163</td>\n",
       "      <td>70</td>\n",
       "      <td>NaN</td>\n",
       "      <td>70</td>\n",
       "      <td>33</td>\n",
       "      <td>70</td>\n",
       "      <td>4'20\"</td>\n",
       "      <td>70</td>\n",
       "      <td>4'15\"</td>\n",
       "      <td>70</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>10</td>\n",
       "      <td>3080</td>\n",
       "      <td>68</td>\n",
       "      <td>2150</td>\n",
       "      <td>68</td>\n",
       "      <td>8.7</td>\n",
       "      <td>68</td>\n",
       "      <td>9.8</td>\n",
       "      <td>68</td>\n",
       "      <td>6.6</td>\n",
       "      <td>...</td>\n",
       "      <td>160</td>\n",
       "      <td>68</td>\n",
       "      <td>9.0</td>\n",
       "      <td>68</td>\n",
       "      <td>31</td>\n",
       "      <td>68</td>\n",
       "      <td>4'25\"</td>\n",
       "      <td>68</td>\n",
       "      <td>4'20\"</td>\n",
       "      <td>68</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>11</td>\n",
       "      <td>2960</td>\n",
       "      <td>66</td>\n",
       "      <td>2050</td>\n",
       "      <td>66</td>\n",
       "      <td>8.9</td>\n",
       "      <td>66</td>\n",
       "      <td>10.0</td>\n",
       "      <td>66</td>\n",
       "      <td>5.2</td>\n",
       "      <td>...</td>\n",
       "      <td>157</td>\n",
       "      <td>66</td>\n",
       "      <td>NaN</td>\n",
       "      <td>66</td>\n",
       "      <td>29</td>\n",
       "      <td>66</td>\n",
       "      <td>4'30\"</td>\n",
       "      <td>66</td>\n",
       "      <td>4'25\"</td>\n",
       "      <td>66</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>12</td>\n",
       "      <td>2840</td>\n",
       "      <td>64</td>\n",
       "      <td>1950</td>\n",
       "      <td>64</td>\n",
       "      <td>9.1</td>\n",
       "      <td>64</td>\n",
       "      <td>10.2</td>\n",
       "      <td>64</td>\n",
       "      <td>3.8</td>\n",
       "      <td>...</td>\n",
       "      <td>154</td>\n",
       "      <td>64</td>\n",
       "      <td>8.0</td>\n",
       "      <td>64</td>\n",
       "      <td>27</td>\n",
       "      <td>64</td>\n",
       "      <td>4'35\"</td>\n",
       "      <td>64</td>\n",
       "      <td>4'30\"</td>\n",
       "      <td>64</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13</th>\n",
       "      <td>13</td>\n",
       "      <td>2720</td>\n",
       "      <td>62</td>\n",
       "      <td>1850</td>\n",
       "      <td>62</td>\n",
       "      <td>9.3</td>\n",
       "      <td>62</td>\n",
       "      <td>10.4</td>\n",
       "      <td>62</td>\n",
       "      <td>2.4</td>\n",
       "      <td>...</td>\n",
       "      <td>151</td>\n",
       "      <td>62</td>\n",
       "      <td>NaN</td>\n",
       "      <td>62</td>\n",
       "      <td>25</td>\n",
       "      <td>62</td>\n",
       "      <td>4'40\"</td>\n",
       "      <td>62</td>\n",
       "      <td>4'35\"</td>\n",
       "      <td>62</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14</th>\n",
       "      <td>14</td>\n",
       "      <td>2600</td>\n",
       "      <td>60</td>\n",
       "      <td>1750</td>\n",
       "      <td>60</td>\n",
       "      <td>9.5</td>\n",
       "      <td>60</td>\n",
       "      <td>10.6</td>\n",
       "      <td>60</td>\n",
       "      <td>1.0</td>\n",
       "      <td>...</td>\n",
       "      <td>148</td>\n",
       "      <td>60</td>\n",
       "      <td>7.0</td>\n",
       "      <td>60</td>\n",
       "      <td>23</td>\n",
       "      <td>60</td>\n",
       "      <td>4'45\"</td>\n",
       "      <td>60</td>\n",
       "      <td>4'40\"</td>\n",
       "      <td>60</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15</th>\n",
       "      <td>15</td>\n",
       "      <td>2470</td>\n",
       "      <td>50</td>\n",
       "      <td>1710</td>\n",
       "      <td>50</td>\n",
       "      <td>9.7</td>\n",
       "      <td>50</td>\n",
       "      <td>10.8</td>\n",
       "      <td>50</td>\n",
       "      <td>0.0</td>\n",
       "      <td>...</td>\n",
       "      <td>143</td>\n",
       "      <td>50</td>\n",
       "      <td>6.0</td>\n",
       "      <td>50</td>\n",
       "      <td>21</td>\n",
       "      <td>50</td>\n",
       "      <td>5'05\"</td>\n",
       "      <td>50</td>\n",
       "      <td>4'50\"</td>\n",
       "      <td>50</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16</th>\n",
       "      <td>16</td>\n",
       "      <td>2340</td>\n",
       "      <td>40</td>\n",
       "      <td>1670</td>\n",
       "      <td>40</td>\n",
       "      <td>9.9</td>\n",
       "      <td>40</td>\n",
       "      <td>11.0</td>\n",
       "      <td>40</td>\n",
       "      <td>-1.0</td>\n",
       "      <td>...</td>\n",
       "      <td>138</td>\n",
       "      <td>40</td>\n",
       "      <td>5.0</td>\n",
       "      <td>40</td>\n",
       "      <td>19</td>\n",
       "      <td>40</td>\n",
       "      <td>5'25\"</td>\n",
       "      <td>40</td>\n",
       "      <td>5'00\"</td>\n",
       "      <td>40</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>17</th>\n",
       "      <td>17</td>\n",
       "      <td>2210</td>\n",
       "      <td>30</td>\n",
       "      <td>1630</td>\n",
       "      <td>30</td>\n",
       "      <td>10.1</td>\n",
       "      <td>30</td>\n",
       "      <td>11.2</td>\n",
       "      <td>30</td>\n",
       "      <td>-2.0</td>\n",
       "      <td>...</td>\n",
       "      <td>133</td>\n",
       "      <td>30</td>\n",
       "      <td>4.0</td>\n",
       "      <td>30</td>\n",
       "      <td>17</td>\n",
       "      <td>30</td>\n",
       "      <td>5'45\"</td>\n",
       "      <td>30</td>\n",
       "      <td>5'10\"</td>\n",
       "      <td>30</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>18</th>\n",
       "      <td>18</td>\n",
       "      <td>2080</td>\n",
       "      <td>20</td>\n",
       "      <td>1590</td>\n",
       "      <td>20</td>\n",
       "      <td>10.3</td>\n",
       "      <td>20</td>\n",
       "      <td>11.4</td>\n",
       "      <td>20</td>\n",
       "      <td>-3.0</td>\n",
       "      <td>...</td>\n",
       "      <td>128</td>\n",
       "      <td>20</td>\n",
       "      <td>3.0</td>\n",
       "      <td>20</td>\n",
       "      <td>15</td>\n",
       "      <td>20</td>\n",
       "      <td>6'05\"</td>\n",
       "      <td>20</td>\n",
       "      <td>5'20\"</td>\n",
       "      <td>20</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>19</th>\n",
       "      <td>19</td>\n",
       "      <td>1950</td>\n",
       "      <td>10</td>\n",
       "      <td>1550</td>\n",
       "      <td>10</td>\n",
       "      <td>10.5</td>\n",
       "      <td>10</td>\n",
       "      <td>11.6</td>\n",
       "      <td>10</td>\n",
       "      <td>-4.0</td>\n",
       "      <td>...</td>\n",
       "      <td>123</td>\n",
       "      <td>10</td>\n",
       "      <td>2.0</td>\n",
       "      <td>10</td>\n",
       "      <td>13</td>\n",
       "      <td>10</td>\n",
       "      <td>6'25\"</td>\n",
       "      <td>10</td>\n",
       "      <td>5'30\"</td>\n",
       "      <td>10</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>20 rows × 25 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "   index  男肺活量       女肺活量      男50米跑      女50米跑       男体前屈  ...  女跳远       \\\n",
       "            成绩   分数    成绩   分数    成绩   分数    成绩   分数    成绩  ...   成绩   分数   \n",
       "0      0  4540  100  3150  100   7.1  100   7.8  100  23.6  ...  204  100   \n",
       "1      1  4420   95  3100   95   7.2   95   7.9   95  21.5  ...  198   95   \n",
       "2      2  4300   90  3050   90   7.3   90   8.0   90  19.4  ...  192   90   \n",
       "3      3  4050   85  2900   85   7.4   85   8.3   85  17.2  ...  185   85   \n",
       "4      4  3800   80  2750   80   7.5   80   8.6   80  15.0  ...  178   80   \n",
       "5      5  3680   78  2650   78   7.7   78   8.8   78  13.6  ...  175   78   \n",
       "6      6  3560   76  2550   76   7.9   76   9.0   76  12.2  ...  172   76   \n",
       "7      7  3440   74  2450   74   8.1   74   9.2   74  10.8  ...  169   74   \n",
       "8      8  3320   72  2350   72   8.3   72   9.4   72   9.4  ...  166   72   \n",
       "9      9  3200   70  2250   70   8.5   70   9.6   70   8.0  ...  163   70   \n",
       "10    10  3080   68  2150   68   8.7   68   9.8   68   6.6  ...  160   68   \n",
       "11    11  2960   66  2050   66   8.9   66  10.0   66   5.2  ...  157   66   \n",
       "12    12  2840   64  1950   64   9.1   64  10.2   64   3.8  ...  154   64   \n",
       "13    13  2720   62  1850   62   9.3   62  10.4   62   2.4  ...  151   62   \n",
       "14    14  2600   60  1750   60   9.5   60  10.6   60   1.0  ...  148   60   \n",
       "15    15  2470   50  1710   50   9.7   50  10.8   50   0.0  ...  143   50   \n",
       "16    16  2340   40  1670   40   9.9   40  11.0   40  -1.0  ...  138   40   \n",
       "17    17  2210   30  1630   30  10.1   30  11.2   30  -2.0  ...  133   30   \n",
       "18    18  2080   20  1590   20  10.3   20  11.4   20  -3.0  ...  128   20   \n",
       "19    19  1950   10  1550   10  10.5   10  11.6   10  -4.0  ...  123   10   \n",
       "\n",
       "     男引体      女仰卧      男1000米跑      女800米跑       \n",
       "      成绩   分数  成绩   分数      成绩   分数     成绩   分数  \n",
       "0   16.0  100  53  100   3'30\"  100  3'24\"  100  \n",
       "1   15.0   95  51   95   3'35\"   95  3'30\"   95  \n",
       "2   14.0   90  49   90   3'40\"   90  3'36\"   90  \n",
       "3   13.0   85  46   85   3'47\"   85  3'43\"   85  \n",
       "4   12.0   80  43   80   3'55\"   80  3'50\"   80  \n",
       "5    NaN   78  41   78   4'00\"   78  3'55\"   78  \n",
       "6   11.0   76  39   76   4'05\"   76  4'00\"   76  \n",
       "7    NaN   74  37   74   4'10\"   74  4'05\"   74  \n",
       "8   10.0   72  35   72   4'15\"   72  4'10\"   72  \n",
       "9    NaN   70  33   70   4'20\"   70  4'15\"   70  \n",
       "10   9.0   68  31   68   4'25\"   68  4'20\"   68  \n",
       "11   NaN   66  29   66   4'30\"   66  4'25\"   66  \n",
       "12   8.0   64  27   64   4'35\"   64  4'30\"   64  \n",
       "13   NaN   62  25   62   4'40\"   62  4'35\"   62  \n",
       "14   7.0   60  23   60   4'45\"   60  4'40\"   60  \n",
       "15   6.0   50  21   50   5'05\"   50  4'50\"   50  \n",
       "16   5.0   40  19   40   5'25\"   40  5'00\"   40  \n",
       "17   4.0   30  17   30   5'45\"   30  5'10\"   30  \n",
       "18   3.0   20  15   20   6'05\"   20  5'20\"   20  \n",
       "19   2.0   10  13   10   6'25\"   10  5'30\"   10  \n",
       "\n",
       "[20 rows x 25 columns]"
      ]
     },
     "execution_count": 91,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "stand.reset_index()  # 重置索引"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "eb7320e3",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 52,
   "id": "b328d8cd",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>班级</th>\n",
       "      <th>性别</th>\n",
       "      <th>男1000米跑</th>\n",
       "      <th>男50米跑</th>\n",
       "      <th>跳远</th>\n",
       "      <th>体前屈</th>\n",
       "      <th>引体</th>\n",
       "      <th>肺活量</th>\n",
       "      <th>身高</th>\n",
       "      <th>体重</th>\n",
       "      <th>BMI</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1</td>\n",
       "      <td>男</td>\n",
       "      <td>4.13</td>\n",
       "      <td>8.88</td>\n",
       "      <td>195.0</td>\n",
       "      <td>12.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>2785.0</td>\n",
       "      <td>170.0</td>\n",
       "      <td>72.6</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1</td>\n",
       "      <td>男</td>\n",
       "      <td>4.16</td>\n",
       "      <td>7.70</td>\n",
       "      <td>225.0</td>\n",
       "      <td>11.0</td>\n",
       "      <td>7.0</td>\n",
       "      <td>3133.0</td>\n",
       "      <td>174.0</td>\n",
       "      <td>52.7</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>1</td>\n",
       "      <td>男</td>\n",
       "      <td>4.09</td>\n",
       "      <td>8.45</td>\n",
       "      <td>218.0</td>\n",
       "      <td>14.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>3901.0</td>\n",
       "      <td>169.0</td>\n",
       "      <td>46.5</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>1</td>\n",
       "      <td>男</td>\n",
       "      <td>4.21</td>\n",
       "      <td>8.05</td>\n",
       "      <td>206.0</td>\n",
       "      <td>13.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>4946.0</td>\n",
       "      <td>183.0</td>\n",
       "      <td>79.7</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>1</td>\n",
       "      <td>男</td>\n",
       "      <td>3.44</td>\n",
       "      <td>7.52</td>\n",
       "      <td>210.0</td>\n",
       "      <td>13.0</td>\n",
       "      <td>9.0</td>\n",
       "      <td>3538.0</td>\n",
       "      <td>171.0</td>\n",
       "      <td>54.7</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>472</th>\n",
       "      <td>17</td>\n",
       "      <td>男</td>\n",
       "      <td>4.23</td>\n",
       "      <td>8.27</td>\n",
       "      <td>208.0</td>\n",
       "      <td>10.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>4647.0</td>\n",
       "      <td>176.0</td>\n",
       "      <td>69.5</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>473</th>\n",
       "      <td>17</td>\n",
       "      <td>男</td>\n",
       "      <td>5.19</td>\n",
       "      <td>9.55</td>\n",
       "      <td>210.0</td>\n",
       "      <td>15.0</td>\n",
       "      <td>6.0</td>\n",
       "      <td>7042.0</td>\n",
       "      <td>177.0</td>\n",
       "      <td>76.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>474</th>\n",
       "      <td>17</td>\n",
       "      <td>男</td>\n",
       "      <td>3.25</td>\n",
       "      <td>7.50</td>\n",
       "      <td>252.0</td>\n",
       "      <td>13.0</td>\n",
       "      <td>13.0</td>\n",
       "      <td>5755.0</td>\n",
       "      <td>181.0</td>\n",
       "      <td>65.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>475</th>\n",
       "      <td>17</td>\n",
       "      <td>男</td>\n",
       "      <td>4.39</td>\n",
       "      <td>7.81</td>\n",
       "      <td>208.0</td>\n",
       "      <td>14.0</td>\n",
       "      <td>11.0</td>\n",
       "      <td>5688.0</td>\n",
       "      <td>172.0</td>\n",
       "      <td>51.7</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>476</th>\n",
       "      <td>17</td>\n",
       "      <td>男</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>477 rows × 11 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "     班级 性别  男1000米跑  男50米跑     跳远   体前屈    引体     肺活量     身高    体重  BMI\n",
       "0     1  男     4.13   8.88  195.0  12.0   1.0  2785.0  170.0  72.6  0.0\n",
       "1     1  男     4.16   7.70  225.0  11.0   7.0  3133.0  174.0  52.7  0.0\n",
       "2     1  男     4.09   8.45  218.0  14.0   1.0  3901.0  169.0  46.5  0.0\n",
       "3     1  男     4.21   8.05  206.0  13.0   1.0  4946.0  183.0  79.7  0.0\n",
       "4     1  男     3.44   7.52  210.0  13.0   9.0  3538.0  171.0  54.7  0.0\n",
       "..   .. ..      ...    ...    ...   ...   ...     ...    ...   ...  ...\n",
       "472  17  男     4.23   8.27  208.0  10.0   0.0  4647.0  176.0  69.5  0.0\n",
       "473  17  男     5.19   9.55  210.0  15.0   6.0  7042.0  177.0  76.0  0.0\n",
       "474  17  男     3.25   7.50  252.0  13.0  13.0  5755.0  181.0  65.0  0.0\n",
       "475  17  男     4.39   7.81  208.0  14.0  11.0  5688.0  172.0  51.7  0.0\n",
       "476  17  男      NaN   0.00    0.0   0.0   0.0     0.0    0.0   0.0  0.0\n",
       "\n",
       "[477 rows x 11 columns]"
      ]
     },
     "execution_count": 52,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "boy.columns =['班级', '性别', '男1000米跑', '男50米跑', '跳远', '体前屈', '引体', '肺活量', '身高', '体重',\n",
    "       'BMI']\n",
    "boy"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 53,
   "id": "6e935d36",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "班级           int64\n",
       "性别          object\n",
       "男1000米跑    float64\n",
       "男50米跑      float64\n",
       "跳远         float64\n",
       "体前屈        float64\n",
       "引体         float64\n",
       "肺活量        float64\n",
       "身高         float64\n",
       "体重         float64\n",
       "BMI        float64\n",
       "dtype: object"
      ]
     },
     "execution_count": 53,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "boy.dtypes"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 54,
   "id": "75cae10a",
   "metadata": {},
   "outputs": [],
   "source": [
    "for col in [ '男1000米跑', '男50米跑']:\n",
    "    \n",
    "   s = stand[col]   # 获取成绩的标准\n",
    "    \n",
    "   def convert(x):\n",
    "       for i in range(len(s)):   # 获取长度循环\n",
    "           if x <= s['成绩'].iloc[0]:\n",
    "               if x == 0:       # 判断是否没有成绩\n",
    "                   return 0    \n",
    "               return 100 \n",
    "           elif x > s['成绩'].iloc[-1]:\n",
    "               return 0     # 跑得太慢\n",
    "           elif (x > s['成绩'].iloc[i - 1]) and (x <= s['成绩'].iloc[i]):\n",
    "               return s['分数'].iloc[i]\n",
    "        \n",
    "         \n",
    "   boy[col + '成绩'] = boy[col].map(convert)   # 增加一列\n",
    "    \n",
    "    \n",
    "  "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 55,
   "id": "2116e4e7",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Index(['班级', '性别', '男1000米跑', '男50米跑', '跳远', '体前屈', '引体', '肺活量', '身高', '体重',\n",
       "       'BMI', '男1000米跑成绩', '男50米跑成绩'],\n",
       "      dtype='object')"
      ]
     },
     "execution_count": 55,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "boy.columns"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 56,
   "id": "f79a26e8",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead tr th {\n",
       "        text-align: left;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr>\n",
       "      <th></th>\n",
       "      <th colspan=\"2\" halign=\"left\">男肺活量</th>\n",
       "      <th colspan=\"2\" halign=\"left\">女肺活量</th>\n",
       "      <th colspan=\"2\" halign=\"left\">男50米跑</th>\n",
       "      <th colspan=\"2\" halign=\"left\">女50米跑</th>\n",
       "      <th colspan=\"2\" halign=\"left\">男体前屈</th>\n",
       "      <th>...</th>\n",
       "      <th colspan=\"2\" halign=\"left\">女跳远</th>\n",
       "      <th colspan=\"2\" halign=\"left\">男引体</th>\n",
       "      <th colspan=\"2\" halign=\"left\">女仰卧</th>\n",
       "      <th colspan=\"2\" halign=\"left\">男1000米跑</th>\n",
       "      <th colspan=\"2\" halign=\"left\">女800米跑</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th></th>\n",
       "      <th>成绩</th>\n",
       "      <th>分数</th>\n",
       "      <th>成绩</th>\n",
       "      <th>分数</th>\n",
       "      <th>成绩</th>\n",
       "      <th>分数</th>\n",
       "      <th>成绩</th>\n",
       "      <th>分数</th>\n",
       "      <th>成绩</th>\n",
       "      <th>分数</th>\n",
       "      <th>...</th>\n",
       "      <th>成绩</th>\n",
       "      <th>分数</th>\n",
       "      <th>成绩</th>\n",
       "      <th>分数</th>\n",
       "      <th>成绩</th>\n",
       "      <th>分数</th>\n",
       "      <th>成绩</th>\n",
       "      <th>分数</th>\n",
       "      <th>成绩</th>\n",
       "      <th>分数</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>4540.0</td>\n",
       "      <td>100.0</td>\n",
       "      <td>3150.0</td>\n",
       "      <td>100.0</td>\n",
       "      <td>7.1</td>\n",
       "      <td>100.0</td>\n",
       "      <td>7.8</td>\n",
       "      <td>100.0</td>\n",
       "      <td>23.6</td>\n",
       "      <td>100.0</td>\n",
       "      <td>...</td>\n",
       "      <td>204.0</td>\n",
       "      <td>100.0</td>\n",
       "      <td>16.0</td>\n",
       "      <td>100.0</td>\n",
       "      <td>53.0</td>\n",
       "      <td>100.0</td>\n",
       "      <td>3.30</td>\n",
       "      <td>100.0</td>\n",
       "      <td>3.24</td>\n",
       "      <td>100.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>4420.0</td>\n",
       "      <td>95.0</td>\n",
       "      <td>3100.0</td>\n",
       "      <td>95.0</td>\n",
       "      <td>7.2</td>\n",
       "      <td>95.0</td>\n",
       "      <td>7.9</td>\n",
       "      <td>95.0</td>\n",
       "      <td>21.5</td>\n",
       "      <td>95.0</td>\n",
       "      <td>...</td>\n",
       "      <td>198.0</td>\n",
       "      <td>95.0</td>\n",
       "      <td>15.0</td>\n",
       "      <td>95.0</td>\n",
       "      <td>51.0</td>\n",
       "      <td>95.0</td>\n",
       "      <td>3.35</td>\n",
       "      <td>95.0</td>\n",
       "      <td>3.30</td>\n",
       "      <td>95.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>4300.0</td>\n",
       "      <td>90.0</td>\n",
       "      <td>3050.0</td>\n",
       "      <td>90.0</td>\n",
       "      <td>7.3</td>\n",
       "      <td>90.0</td>\n",
       "      <td>8.0</td>\n",
       "      <td>90.0</td>\n",
       "      <td>19.4</td>\n",
       "      <td>90.0</td>\n",
       "      <td>...</td>\n",
       "      <td>192.0</td>\n",
       "      <td>90.0</td>\n",
       "      <td>14.0</td>\n",
       "      <td>90.0</td>\n",
       "      <td>49.0</td>\n",
       "      <td>90.0</td>\n",
       "      <td>3.40</td>\n",
       "      <td>90.0</td>\n",
       "      <td>3.36</td>\n",
       "      <td>90.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>4050.0</td>\n",
       "      <td>85.0</td>\n",
       "      <td>2900.0</td>\n",
       "      <td>85.0</td>\n",
       "      <td>7.4</td>\n",
       "      <td>85.0</td>\n",
       "      <td>8.3</td>\n",
       "      <td>85.0</td>\n",
       "      <td>17.2</td>\n",
       "      <td>85.0</td>\n",
       "      <td>...</td>\n",
       "      <td>185.0</td>\n",
       "      <td>85.0</td>\n",
       "      <td>13.0</td>\n",
       "      <td>85.0</td>\n",
       "      <td>46.0</td>\n",
       "      <td>85.0</td>\n",
       "      <td>3.47</td>\n",
       "      <td>85.0</td>\n",
       "      <td>3.43</td>\n",
       "      <td>85.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>3800.0</td>\n",
       "      <td>80.0</td>\n",
       "      <td>2750.0</td>\n",
       "      <td>80.0</td>\n",
       "      <td>7.5</td>\n",
       "      <td>80.0</td>\n",
       "      <td>8.6</td>\n",
       "      <td>80.0</td>\n",
       "      <td>15.0</td>\n",
       "      <td>80.0</td>\n",
       "      <td>...</td>\n",
       "      <td>178.0</td>\n",
       "      <td>80.0</td>\n",
       "      <td>12.0</td>\n",
       "      <td>80.0</td>\n",
       "      <td>43.0</td>\n",
       "      <td>80.0</td>\n",
       "      <td>3.55</td>\n",
       "      <td>80.0</td>\n",
       "      <td>3.50</td>\n",
       "      <td>80.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>5 rows × 24 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "     男肺活量           女肺活量        男50米跑        女50米跑         男体前屈         ...  \\\n",
       "       成绩     分数      成绩     分数    成绩     分数    成绩     分数    成绩     分数  ...   \n",
       "0  4540.0  100.0  3150.0  100.0   7.1  100.0   7.8  100.0  23.6  100.0  ...   \n",
       "1  4420.0   95.0  3100.0   95.0   7.2   95.0   7.9   95.0  21.5   95.0  ...   \n",
       "2  4300.0   90.0  3050.0   90.0   7.3   90.0   8.0   90.0  19.4   90.0  ...   \n",
       "3  4050.0   85.0  2900.0   85.0   7.4   85.0   8.3   85.0  17.2   85.0  ...   \n",
       "4  3800.0   80.0  2750.0   80.0   7.5   80.0   8.6   80.0  15.0   80.0  ...   \n",
       "\n",
       "     女跳远          男引体          女仰卧        男1000米跑        女800米跑         \n",
       "      成绩     分数    成绩     分数    成绩     分数      成绩     分数     成绩     分数  \n",
       "0  204.0  100.0  16.0  100.0  53.0  100.0    3.30  100.0   3.24  100.0  \n",
       "1  198.0   95.0  15.0   95.0  51.0   95.0    3.35   95.0   3.30   95.0  \n",
       "2  192.0   90.0  14.0   90.0  49.0   90.0    3.40   90.0   3.36   90.0  \n",
       "3  185.0   85.0  13.0   85.0  46.0   85.0    3.47   85.0   3.43   85.0  \n",
       "4  178.0   80.0  12.0   80.0  43.0   80.0    3.55   80.0   3.50   80.0  \n",
       "\n",
       "[5 rows x 24 columns]"
      ]
     },
     "execution_count": 56,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "stand.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 57,
   "id": "eb25f887",
   "metadata": {},
   "outputs": [],
   "source": [
    "for col in ['跳远', '体前屈', '引体', '肺活量']:\n",
    "   s = stand['男' + col]\n",
    "    \n",
    "    \n",
    "   def convert(x):\n",
    "       for i in range(len(s)):\n",
    "           if x >= s['成绩'].iloc[i]:\n",
    "               return s['分数'].iloc[i]\n",
    "       return 0\n",
    "    \n",
    "   boy[col + '成绩'] = boy[col].map(convert)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 58,
   "id": "d8b624e2",
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>班级</th>\n",
       "      <th>性别</th>\n",
       "      <th>男1000米跑</th>\n",
       "      <th>男50米跑</th>\n",
       "      <th>跳远</th>\n",
       "      <th>体前屈</th>\n",
       "      <th>引体</th>\n",
       "      <th>肺活量</th>\n",
       "      <th>身高</th>\n",
       "      <th>体重</th>\n",
       "      <th>BMI</th>\n",
       "      <th>男1000米跑成绩</th>\n",
       "      <th>男50米跑成绩</th>\n",
       "      <th>跳远成绩</th>\n",
       "      <th>体前屈成绩</th>\n",
       "      <th>引体成绩</th>\n",
       "      <th>肺活量成绩</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1</td>\n",
       "      <td>男</td>\n",
       "      <td>4.13</td>\n",
       "      <td>8.88</td>\n",
       "      <td>195.0</td>\n",
       "      <td>12.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>2785.0</td>\n",
       "      <td>170.0</td>\n",
       "      <td>72.6</td>\n",
       "      <td>0.0</td>\n",
       "      <td>72.0</td>\n",
       "      <td>66.0</td>\n",
       "      <td>60.0</td>\n",
       "      <td>74.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>62.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1</td>\n",
       "      <td>男</td>\n",
       "      <td>4.16</td>\n",
       "      <td>7.70</td>\n",
       "      <td>225.0</td>\n",
       "      <td>11.0</td>\n",
       "      <td>7.0</td>\n",
       "      <td>3133.0</td>\n",
       "      <td>174.0</td>\n",
       "      <td>52.7</td>\n",
       "      <td>0.0</td>\n",
       "      <td>70.0</td>\n",
       "      <td>78.0</td>\n",
       "      <td>74.0</td>\n",
       "      <td>74.0</td>\n",
       "      <td>60.0</td>\n",
       "      <td>68.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>1</td>\n",
       "      <td>男</td>\n",
       "      <td>4.09</td>\n",
       "      <td>8.45</td>\n",
       "      <td>218.0</td>\n",
       "      <td>14.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>3901.0</td>\n",
       "      <td>169.0</td>\n",
       "      <td>46.5</td>\n",
       "      <td>0.0</td>\n",
       "      <td>74.0</td>\n",
       "      <td>70.0</td>\n",
       "      <td>70.0</td>\n",
       "      <td>78.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>80.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>1</td>\n",
       "      <td>男</td>\n",
       "      <td>4.21</td>\n",
       "      <td>8.05</td>\n",
       "      <td>206.0</td>\n",
       "      <td>13.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>4946.0</td>\n",
       "      <td>183.0</td>\n",
       "      <td>79.7</td>\n",
       "      <td>0.0</td>\n",
       "      <td>68.0</td>\n",
       "      <td>74.0</td>\n",
       "      <td>64.0</td>\n",
       "      <td>76.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>100.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>1</td>\n",
       "      <td>男</td>\n",
       "      <td>3.44</td>\n",
       "      <td>7.52</td>\n",
       "      <td>210.0</td>\n",
       "      <td>13.0</td>\n",
       "      <td>9.0</td>\n",
       "      <td>3538.0</td>\n",
       "      <td>171.0</td>\n",
       "      <td>54.7</td>\n",
       "      <td>0.0</td>\n",
       "      <td>85.0</td>\n",
       "      <td>78.0</td>\n",
       "      <td>66.0</td>\n",
       "      <td>76.0</td>\n",
       "      <td>68.0</td>\n",
       "      <td>74.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   班级 性别  男1000米跑  男50米跑     跳远   体前屈   引体     肺活量     身高    体重  BMI  \\\n",
       "0   1  男     4.13   8.88  195.0  12.0  1.0  2785.0  170.0  72.6  0.0   \n",
       "1   1  男     4.16   7.70  225.0  11.0  7.0  3133.0  174.0  52.7  0.0   \n",
       "2   1  男     4.09   8.45  218.0  14.0  1.0  3901.0  169.0  46.5  0.0   \n",
       "3   1  男     4.21   8.05  206.0  13.0  1.0  4946.0  183.0  79.7  0.0   \n",
       "4   1  男     3.44   7.52  210.0  13.0  9.0  3538.0  171.0  54.7  0.0   \n",
       "\n",
       "   男1000米跑成绩  男50米跑成绩  跳远成绩  体前屈成绩  引体成绩  肺活量成绩  \n",
       "0       72.0     66.0  60.0   74.0   0.0   62.0  \n",
       "1       70.0     78.0  74.0   74.0  60.0   68.0  \n",
       "2       74.0     70.0  70.0   78.0   0.0   80.0  \n",
       "3       68.0     74.0  64.0   76.0   0.0  100.0  \n",
       "4       85.0     78.0  66.0   76.0  68.0   74.0  "
      ]
     },
     "execution_count": 58,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "boy.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 59,
   "id": "73e21a3b",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Index(['班级', '性别', '男1000米跑', '男50米跑', '跳远', '体前屈', '引体', '肺活量', '身高', '体重',\n",
       "       'BMI', '男1000米跑成绩', '男50米跑成绩', '跳远成绩', '体前屈成绩', '引体成绩', '肺活量成绩'],\n",
       "      dtype='object')"
      ]
     },
     "execution_count": 59,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "boy.columns"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 97,
   "id": "c8e76f34",
   "metadata": {},
   "outputs": [],
   "source": [
    "cols = ['班级', '性别','男1000米跑','男1000米跑成绩', '男50米跑', '男50米跑成绩', '跳远', '跳远成绩','体前屈',\n",
    "         '体前屈成绩', '引体','引体成绩', '肺活量', '肺活量成绩', '身高','体重', 'BMI', ]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 98,
   "id": "5323a3e6",
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>班级</th>\n",
       "      <th>性别</th>\n",
       "      <th>男1000米跑</th>\n",
       "      <th>男1000米跑成绩</th>\n",
       "      <th>男50米跑</th>\n",
       "      <th>男50米跑成绩</th>\n",
       "      <th>跳远</th>\n",
       "      <th>跳远成绩</th>\n",
       "      <th>体前屈</th>\n",
       "      <th>体前屈成绩</th>\n",
       "      <th>引体</th>\n",
       "      <th>引体成绩</th>\n",
       "      <th>肺活量</th>\n",
       "      <th>肺活量成绩</th>\n",
       "      <th>身高</th>\n",
       "      <th>体重</th>\n",
       "      <th>BMI</th>\n",
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       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1</td>\n",
       "      <td>男</td>\n",
       "      <td>4.13</td>\n",
       "      <td>72.0</td>\n",
       "      <td>8.88</td>\n",
       "      <td>66.0</td>\n",
       "      <td>195.0</td>\n",
       "      <td>60.0</td>\n",
       "      <td>12.0</td>\n",
       "      <td>74.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>2785.0</td>\n",
       "      <td>62.0</td>\n",
       "      <td>1.70</td>\n",
       "      <td>72.6</td>\n",
       "      <td>25.12</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1</td>\n",
       "      <td>男</td>\n",
       "      <td>4.16</td>\n",
       "      <td>70.0</td>\n",
       "      <td>7.70</td>\n",
       "      <td>78.0</td>\n",
       "      <td>225.0</td>\n",
       "      <td>74.0</td>\n",
       "      <td>11.0</td>\n",
       "      <td>74.0</td>\n",
       "      <td>7.0</td>\n",
       "      <td>60.0</td>\n",
       "      <td>3133.0</td>\n",
       "      <td>68.0</td>\n",
       "      <td>1.74</td>\n",
       "      <td>52.7</td>\n",
       "      <td>17.41</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>1</td>\n",
       "      <td>男</td>\n",
       "      <td>4.09</td>\n",
       "      <td>74.0</td>\n",
       "      <td>8.45</td>\n",
       "      <td>70.0</td>\n",
       "      <td>218.0</td>\n",
       "      <td>70.0</td>\n",
       "      <td>14.0</td>\n",
       "      <td>78.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>3901.0</td>\n",
       "      <td>80.0</td>\n",
       "      <td>1.69</td>\n",
       "      <td>46.5</td>\n",
       "      <td>16.28</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>1</td>\n",
       "      <td>男</td>\n",
       "      <td>4.21</td>\n",
       "      <td>68.0</td>\n",
       "      <td>8.05</td>\n",
       "      <td>74.0</td>\n",
       "      <td>206.0</td>\n",
       "      <td>64.0</td>\n",
       "      <td>13.0</td>\n",
       "      <td>76.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>4946.0</td>\n",
       "      <td>100.0</td>\n",
       "      <td>1.83</td>\n",
       "      <td>79.7</td>\n",
       "      <td>23.80</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>1</td>\n",
       "      <td>男</td>\n",
       "      <td>3.44</td>\n",
       "      <td>85.0</td>\n",
       "      <td>7.52</td>\n",
       "      <td>78.0</td>\n",
       "      <td>210.0</td>\n",
       "      <td>66.0</td>\n",
       "      <td>13.0</td>\n",
       "      <td>76.0</td>\n",
       "      <td>9.0</td>\n",
       "      <td>68.0</td>\n",
       "      <td>3538.0</td>\n",
       "      <td>74.0</td>\n",
       "      <td>1.71</td>\n",
       "      <td>54.7</td>\n",
       "      <td>18.71</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>472</th>\n",
       "      <td>17</td>\n",
       "      <td>男</td>\n",
       "      <td>4.23</td>\n",
       "      <td>68.0</td>\n",
       "      <td>8.27</td>\n",
       "      <td>72.0</td>\n",
       "      <td>208.0</td>\n",
       "      <td>66.0</td>\n",
       "      <td>10.0</td>\n",
       "      <td>72.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>4647.0</td>\n",
       "      <td>100.0</td>\n",
       "      <td>1.76</td>\n",
       "      <td>69.5</td>\n",
       "      <td>22.44</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>473</th>\n",
       "      <td>17</td>\n",
       "      <td>男</td>\n",
       "      <td>5.19</td>\n",
       "      <td>40.0</td>\n",
       "      <td>9.55</td>\n",
       "      <td>50.0</td>\n",
       "      <td>210.0</td>\n",
       "      <td>66.0</td>\n",
       "      <td>15.0</td>\n",
       "      <td>80.0</td>\n",
       "      <td>6.0</td>\n",
       "      <td>50.0</td>\n",
       "      <td>7042.0</td>\n",
       "      <td>100.0</td>\n",
       "      <td>1.77</td>\n",
       "      <td>76.0</td>\n",
       "      <td>24.26</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>474</th>\n",
       "      <td>17</td>\n",
       "      <td>男</td>\n",
       "      <td>3.25</td>\n",
       "      <td>100.0</td>\n",
       "      <td>7.50</td>\n",
       "      <td>80.0</td>\n",
       "      <td>252.0</td>\n",
       "      <td>90.0</td>\n",
       "      <td>13.0</td>\n",
       "      <td>76.0</td>\n",
       "      <td>13.0</td>\n",
       "      <td>85.0</td>\n",
       "      <td>5755.0</td>\n",
       "      <td>100.0</td>\n",
       "      <td>1.81</td>\n",
       "      <td>65.0</td>\n",
       "      <td>19.84</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>475</th>\n",
       "      <td>17</td>\n",
       "      <td>男</td>\n",
       "      <td>4.39</td>\n",
       "      <td>62.0</td>\n",
       "      <td>7.81</td>\n",
       "      <td>76.0</td>\n",
       "      <td>208.0</td>\n",
       "      <td>66.0</td>\n",
       "      <td>14.0</td>\n",
       "      <td>78.0</td>\n",
       "      <td>11.0</td>\n",
       "      <td>76.0</td>\n",
       "      <td>5688.0</td>\n",
       "      <td>100.0</td>\n",
       "      <td>1.72</td>\n",
       "      <td>51.7</td>\n",
       "      <td>17.48</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>476</th>\n",
       "      <td>17</td>\n",
       "      <td>男</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>50.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.0</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>477 rows × 17 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "     班级 性别  男1000米跑  男1000米跑成绩  男50米跑  男50米跑成绩     跳远  跳远成绩   体前屈  体前屈成绩  \\\n",
       "0     1  男     4.13       72.0   8.88     66.0  195.0  60.0  12.0   74.0   \n",
       "1     1  男     4.16       70.0   7.70     78.0  225.0  74.0  11.0   74.0   \n",
       "2     1  男     4.09       74.0   8.45     70.0  218.0  70.0  14.0   78.0   \n",
       "3     1  男     4.21       68.0   8.05     74.0  206.0  64.0  13.0   76.0   \n",
       "4     1  男     3.44       85.0   7.52     78.0  210.0  66.0  13.0   76.0   \n",
       "..   .. ..      ...        ...    ...      ...    ...   ...   ...    ...   \n",
       "472  17  男     4.23       68.0   8.27     72.0  208.0  66.0  10.0   72.0   \n",
       "473  17  男     5.19       40.0   9.55     50.0  210.0  66.0  15.0   80.0   \n",
       "474  17  男     3.25      100.0   7.50     80.0  252.0  90.0  13.0   76.0   \n",
       "475  17  男     4.39       62.0   7.81     76.0  208.0  66.0  14.0   78.0   \n",
       "476  17  男      NaN        NaN   0.00      0.0    0.0   0.0   0.0   50.0   \n",
       "\n",
       "       引体  引体成绩     肺活量  肺活量成绩    身高    体重    BMI  \n",
       "0     1.0   0.0  2785.0   62.0  1.70  72.6  25.12  \n",
       "1     7.0  60.0  3133.0   68.0  1.74  52.7  17.41  \n",
       "2     1.0   0.0  3901.0   80.0  1.69  46.5  16.28  \n",
       "3     1.0   0.0  4946.0  100.0  1.83  79.7  23.80  \n",
       "4     9.0  68.0  3538.0   74.0  1.71  54.7  18.71  \n",
       "..    ...   ...     ...    ...   ...   ...    ...  \n",
       "472   0.0   0.0  4647.0  100.0  1.76  69.5  22.44  \n",
       "473   6.0  50.0  7042.0  100.0  1.77  76.0  24.26  \n",
       "474  13.0  85.0  5755.0  100.0  1.81  65.0  19.84  \n",
       "475  11.0  76.0  5688.0  100.0  1.72  51.7  17.48  \n",
       "476   0.0   0.0     0.0    0.0  0.00   0.0    NaN  \n",
       "\n",
       "[477 rows x 17 columns]"
      ]
     },
     "execution_count": 98,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "boy[cols]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "2850275c",
   "metadata": {},
   "outputs": [],
   "source": [
    "# 计算体重"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "id": "aa283b8a",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0     170.000000\n",
       "1     174.000000\n",
       "2     169.000000\n",
       "3     183.000000\n",
       "4     171.000000\n",
       "5     175.000000\n",
       "6     173.000000\n",
       "7     178.000000\n",
       "8     175.000000\n",
       "9     167.000000\n",
       "10    173.000000\n",
       "11    163.000000\n",
       "12    179.000000\n",
       "13    177.000000\n",
       "14      0.000000\n",
       "15    174.000000\n",
       "16    179.000000\n",
       "17    183.000000\n",
       "18    173.000000\n",
       "19    174.000000\n",
       "20    177.000000\n",
       "21    177.000000\n",
       "22    185.000000\n",
       "23    177.000000\n",
       "24    173.000000\n",
       "25    169.000000\n",
       "26    169.000000\n",
       "27    171.000000\n",
       "28    166.000000\n",
       "29    169.000000\n",
       "30    174.000000\n",
       "31    166.000000\n",
       "32    172.000000\n",
       "33    175.000000\n",
       "34      0.000000\n",
       "35    174.000000\n",
       "36    182.000000\n",
       "37      0.000000\n",
       "38    173.000000\n",
       "39    164.000000\n",
       "40    184.000003\n",
       "41    175.999999\n",
       "42    184.000003\n",
       "43    174.000001\n",
       "44    176.999998\n",
       "45    170.000005\n",
       "46    166.999996\n",
       "47    172.000003\n",
       "48    162.000000\n",
       "49    163.999999\n",
       "Name: 身高, dtype: float64"
      ]
     },
     "execution_count": 37,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "h = boy['身高'] \n",
    "h[:50]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "736b291f",
   "metadata": {},
   "outputs": [],
   "source": [
    "计算身高"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "id": "a4a36a8e",
   "metadata": {},
   "outputs": [],
   "source": [
    "def convert(x):\n",
    "   if x >100:\n",
    "       return x/100\n",
    "   return x\n",
    "boy['身高'] = boy['身高'].map(convert)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "id": "788ba95f",
   "metadata": {},
   "outputs": [],
   "source": [
    "boy['BMI'] = (boy['体重'] / boy['身高']**2).round(2)  # 保留2位小数"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 68,
   "id": "89bd8da8",
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>班级</th>\n",
       "      <th>性别</th>\n",
       "      <th>男1000米跑</th>\n",
       "      <th>男50米跑</th>\n",
       "      <th>跳远</th>\n",
       "      <th>体前屈</th>\n",
       "      <th>引体</th>\n",
       "      <th>肺活量</th>\n",
       "      <th>身高</th>\n",
       "      <th>体重</th>\n",
       "      <th>BMI</th>\n",
       "      <th>男1000米跑成绩</th>\n",
       "      <th>男50米跑成绩</th>\n",
       "      <th>跳远成绩</th>\n",
       "      <th>体前屈成绩</th>\n",
       "      <th>引体成绩</th>\n",
       "      <th>肺活量成绩</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1</td>\n",
       "      <td>男</td>\n",
       "      <td>4.13</td>\n",
       "      <td>8.88</td>\n",
       "      <td>195.0</td>\n",
       "      <td>12.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>2785.0</td>\n",
       "      <td>1.70</td>\n",
       "      <td>72.6</td>\n",
       "      <td>25.12</td>\n",
       "      <td>72.0</td>\n",
       "      <td>66.0</td>\n",
       "      <td>60.0</td>\n",
       "      <td>74.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>62.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1</td>\n",
       "      <td>男</td>\n",
       "      <td>4.16</td>\n",
       "      <td>7.70</td>\n",
       "      <td>225.0</td>\n",
       "      <td>11.0</td>\n",
       "      <td>7.0</td>\n",
       "      <td>3133.0</td>\n",
       "      <td>1.74</td>\n",
       "      <td>52.7</td>\n",
       "      <td>17.41</td>\n",
       "      <td>70.0</td>\n",
       "      <td>78.0</td>\n",
       "      <td>74.0</td>\n",
       "      <td>74.0</td>\n",
       "      <td>60.0</td>\n",
       "      <td>68.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>1</td>\n",
       "      <td>男</td>\n",
       "      <td>4.09</td>\n",
       "      <td>8.45</td>\n",
       "      <td>218.0</td>\n",
       "      <td>14.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>3901.0</td>\n",
       "      <td>1.69</td>\n",
       "      <td>46.5</td>\n",
       "      <td>16.28</td>\n",
       "      <td>74.0</td>\n",
       "      <td>70.0</td>\n",
       "      <td>70.0</td>\n",
       "      <td>78.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>80.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>1</td>\n",
       "      <td>男</td>\n",
       "      <td>4.21</td>\n",
       "      <td>8.05</td>\n",
       "      <td>206.0</td>\n",
       "      <td>13.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>4946.0</td>\n",
       "      <td>1.83</td>\n",
       "      <td>79.7</td>\n",
       "      <td>23.80</td>\n",
       "      <td>68.0</td>\n",
       "      <td>74.0</td>\n",
       "      <td>64.0</td>\n",
       "      <td>76.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>100.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>1</td>\n",
       "      <td>男</td>\n",
       "      <td>3.44</td>\n",
       "      <td>7.52</td>\n",
       "      <td>210.0</td>\n",
       "      <td>13.0</td>\n",
       "      <td>9.0</td>\n",
       "      <td>3538.0</td>\n",
       "      <td>1.71</td>\n",
       "      <td>54.7</td>\n",
       "      <td>18.71</td>\n",
       "      <td>85.0</td>\n",
       "      <td>78.0</td>\n",
       "      <td>66.0</td>\n",
       "      <td>76.0</td>\n",
       "      <td>68.0</td>\n",
       "      <td>74.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   班级 性别  男1000米跑  男50米跑     跳远   体前屈   引体     肺活量    身高    体重    BMI  \\\n",
       "0   1  男     4.13   8.88  195.0  12.0  1.0  2785.0  1.70  72.6  25.12   \n",
       "1   1  男     4.16   7.70  225.0  11.0  7.0  3133.0  1.74  52.7  17.41   \n",
       "2   1  男     4.09   8.45  218.0  14.0  1.0  3901.0  1.69  46.5  16.28   \n",
       "3   1  男     4.21   8.05  206.0  13.0  1.0  4946.0  1.83  79.7  23.80   \n",
       "4   1  男     3.44   7.52  210.0  13.0  9.0  3538.0  1.71  54.7  18.71   \n",
       "\n",
       "   男1000米跑成绩  男50米跑成绩  跳远成绩  体前屈成绩  引体成绩  肺活量成绩  \n",
       "0       72.0     66.0  60.0   74.0   0.0   62.0  \n",
       "1       70.0     78.0  74.0   74.0  60.0   68.0  \n",
       "2       74.0     70.0  70.0   78.0   0.0   80.0  \n",
       "3       68.0     74.0  64.0   76.0   0.0  100.0  \n",
       "4       85.0     78.0  66.0   76.0  68.0   74.0  "
      ]
     },
     "execution_count": 68,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "boy.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "id": "0ac3033f",
   "metadata": {},
   "outputs": [],
   "source": [
    "def convert_BMI(x):\n",
    "   if x >= 26.4:\n",
    "       return 60\n",
    "   elif (x <= 16.4) or (x > 23.3 and x < 26.3):\n",
    "       return 80\n",
    "   elif x >= 16.5 and x <= 23.2:\n",
    "       return 100\n",
    "   return 0\n",
    "boy['BMI_score'] = boy['BMI'].map(convert_BMI)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 72,
   "id": "e9d74749",
   "metadata": {},
   "outputs": [
    {
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       "      <th>男50米跑成绩</th>\n",
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       "      <td>8.88</td>\n",
       "      <td>195.0</td>\n",
       "      <td>12.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>2785.0</td>\n",
       "      <td>1.70</td>\n",
       "      <td>72.6</td>\n",
       "      <td>25.12</td>\n",
       "      <td>72.0</td>\n",
       "      <td>66.0</td>\n",
       "      <td>60.0</td>\n",
       "      <td>74.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>62.0</td>\n",
       "      <td>80</td>\n",
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       "      <td>男</td>\n",
       "      <td>4.16</td>\n",
       "      <td>7.70</td>\n",
       "      <td>225.0</td>\n",
       "      <td>11.0</td>\n",
       "      <td>7.0</td>\n",
       "      <td>3133.0</td>\n",
       "      <td>1.74</td>\n",
       "      <td>52.7</td>\n",
       "      <td>17.41</td>\n",
       "      <td>70.0</td>\n",
       "      <td>78.0</td>\n",
       "      <td>74.0</td>\n",
       "      <td>74.0</td>\n",
       "      <td>60.0</td>\n",
       "      <td>68.0</td>\n",
       "      <td>100</td>\n",
       "      <td>100</td>\n",
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       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>1</td>\n",
       "      <td>男</td>\n",
       "      <td>4.09</td>\n",
       "      <td>8.45</td>\n",
       "      <td>218.0</td>\n",
       "      <td>14.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>3901.0</td>\n",
       "      <td>1.69</td>\n",
       "      <td>46.5</td>\n",
       "      <td>16.28</td>\n",
       "      <td>74.0</td>\n",
       "      <td>70.0</td>\n",
       "      <td>70.0</td>\n",
       "      <td>78.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>80.0</td>\n",
       "      <td>80</td>\n",
       "      <td>80</td>\n",
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       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>1</td>\n",
       "      <td>男</td>\n",
       "      <td>4.21</td>\n",
       "      <td>8.05</td>\n",
       "      <td>206.0</td>\n",
       "      <td>13.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>4946.0</td>\n",
       "      <td>1.83</td>\n",
       "      <td>79.7</td>\n",
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       "      <td>74.0</td>\n",
       "      <td>64.0</td>\n",
       "      <td>76.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>100.0</td>\n",
       "      <td>80</td>\n",
       "      <td>80</td>\n",
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       "      <td>3.44</td>\n",
       "      <td>7.52</td>\n",
       "      <td>210.0</td>\n",
       "      <td>13.0</td>\n",
       "      <td>9.0</td>\n",
       "      <td>3538.0</td>\n",
       "      <td>1.71</td>\n",
       "      <td>54.7</td>\n",
       "      <td>18.71</td>\n",
       "      <td>85.0</td>\n",
       "      <td>78.0</td>\n",
       "      <td>66.0</td>\n",
       "      <td>76.0</td>\n",
       "      <td>68.0</td>\n",
       "      <td>74.0</td>\n",
       "      <td>100</td>\n",
       "      <td>100</td>\n",
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      "text/plain": [
       "   班级 性别  男1000米跑  男50米跑     跳远   体前屈   引体     肺活量    身高    体重    BMI  \\\n",
       "0   1  男     4.13   8.88  195.0  12.0  1.0  2785.0  1.70  72.6  25.12   \n",
       "1   1  男     4.16   7.70  225.0  11.0  7.0  3133.0  1.74  52.7  17.41   \n",
       "2   1  男     4.09   8.45  218.0  14.0  1.0  3901.0  1.69  46.5  16.28   \n",
       "3   1  男     4.21   8.05  206.0  13.0  1.0  4946.0  1.83  79.7  23.80   \n",
       "4   1  男     3.44   7.52  210.0  13.0  9.0  3538.0  1.71  54.7  18.71   \n",
       "\n",
       "   男1000米跑成绩  男50米跑成绩  跳远成绩  体前屈成绩  引体成绩  肺活量成绩  BMI_score  BMI_stand  \n",
       "0       72.0     66.0  60.0   74.0   0.0   62.0         80         80  \n",
       "1       70.0     78.0  74.0   74.0  60.0   68.0        100        100  \n",
       "2       74.0     70.0  70.0   78.0   0.0   80.0         80         80  \n",
       "3       68.0     74.0  64.0   76.0   0.0  100.0         80         80  \n",
       "4       85.0     78.0  66.0   76.0  68.0   74.0        100        100  "
      ]
     },
     "execution_count": 72,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "boy.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 77,
   "id": "da3b0953",
   "metadata": {},
   "outputs": [
    {
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       "      <td>68.0</td>\n",
       "      <td>8.05</td>\n",
       "      <td>74.0</td>\n",
       "      <td>206.0</td>\n",
       "      <td>64.0</td>\n",
       "      <td>13.0</td>\n",
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       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
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       "      <td>85.0</td>\n",
       "      <td>7.52</td>\n",
       "      <td>78.0</td>\n",
       "      <td>210.0</td>\n",
       "      <td>66.0</td>\n",
       "      <td>13.0</td>\n",
       "      <td>76.0</td>\n",
       "      <td>9.0</td>\n",
       "      <td>68.0</td>\n",
       "      <td>3538.0</td>\n",
       "      <td>74.0</td>\n",
       "      <td>1.71</td>\n",
       "      <td>54.7</td>\n",
       "      <td>18.71</td>\n",
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       "      <td>68.0</td>\n",
       "      <td>8.27</td>\n",
       "      <td>72.0</td>\n",
       "      <td>208.0</td>\n",
       "      <td>66.0</td>\n",
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       "      <td>72.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>4647.0</td>\n",
       "      <td>100.0</td>\n",
       "      <td>1.76</td>\n",
       "      <td>69.5</td>\n",
       "      <td>22.44</td>\n",
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       "      <td>17</td>\n",
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       "      <td>5.19</td>\n",
       "      <td>40.0</td>\n",
       "      <td>9.55</td>\n",
       "      <td>50.0</td>\n",
       "      <td>210.0</td>\n",
       "      <td>66.0</td>\n",
       "      <td>15.0</td>\n",
       "      <td>80.0</td>\n",
       "      <td>6.0</td>\n",
       "      <td>50.0</td>\n",
       "      <td>7042.0</td>\n",
       "      <td>100.0</td>\n",
       "      <td>1.77</td>\n",
       "      <td>76.0</td>\n",
       "      <td>24.26</td>\n",
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       "      <td>100.0</td>\n",
       "      <td>7.50</td>\n",
       "      <td>80.0</td>\n",
       "      <td>252.0</td>\n",
       "      <td>90.0</td>\n",
       "      <td>13.0</td>\n",
       "      <td>76.0</td>\n",
       "      <td>13.0</td>\n",
       "      <td>85.0</td>\n",
       "      <td>5755.0</td>\n",
       "      <td>100.0</td>\n",
       "      <td>1.81</td>\n",
       "      <td>65.0</td>\n",
       "      <td>19.84</td>\n",
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       "    <tr>\n",
       "      <th>475</th>\n",
       "      <td>17</td>\n",
       "      <td>男</td>\n",
       "      <td>4.39</td>\n",
       "      <td>62.0</td>\n",
       "      <td>7.81</td>\n",
       "      <td>76.0</td>\n",
       "      <td>208.0</td>\n",
       "      <td>66.0</td>\n",
       "      <td>14.0</td>\n",
       "      <td>78.0</td>\n",
       "      <td>11.0</td>\n",
       "      <td>76.0</td>\n",
       "      <td>5688.0</td>\n",
       "      <td>100.0</td>\n",
       "      <td>1.72</td>\n",
       "      <td>51.7</td>\n",
       "      <td>17.48</td>\n",
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       "    <tr>\n",
       "      <th>476</th>\n",
       "      <td>17</td>\n",
       "      <td>男</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>50.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.0</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>477 rows × 17 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "     班级 性别  男1000米跑  男1000米跑成绩  男50米跑  男50米跑成绩     跳远  跳远成绩   体前屈  体前屈成绩  \\\n",
       "0     1  男     4.13       72.0   8.88     66.0  195.0  60.0  12.0   74.0   \n",
       "1     1  男     4.16       70.0   7.70     78.0  225.0  74.0  11.0   74.0   \n",
       "2     1  男     4.09       74.0   8.45     70.0  218.0  70.0  14.0   78.0   \n",
       "3     1  男     4.21       68.0   8.05     74.0  206.0  64.0  13.0   76.0   \n",
       "4     1  男     3.44       85.0   7.52     78.0  210.0  66.0  13.0   76.0   \n",
       "..   .. ..      ...        ...    ...      ...    ...   ...   ...    ...   \n",
       "472  17  男     4.23       68.0   8.27     72.0  208.0  66.0  10.0   72.0   \n",
       "473  17  男     5.19       40.0   9.55     50.0  210.0  66.0  15.0   80.0   \n",
       "474  17  男     3.25      100.0   7.50     80.0  252.0  90.0  13.0   76.0   \n",
       "475  17  男     4.39       62.0   7.81     76.0  208.0  66.0  14.0   78.0   \n",
       "476  17  男      NaN        NaN   0.00      0.0    0.0   0.0   0.0   50.0   \n",
       "\n",
       "       引体  引体成绩     肺活量  肺活量成绩    身高    体重    BMI  \n",
       "0     1.0   0.0  2785.0   62.0  1.70  72.6  25.12  \n",
       "1     7.0  60.0  3133.0   68.0  1.74  52.7  17.41  \n",
       "2     1.0   0.0  3901.0   80.0  1.69  46.5  16.28  \n",
       "3     1.0   0.0  4946.0  100.0  1.83  79.7  23.80  \n",
       "4     9.0  68.0  3538.0   74.0  1.71  54.7  18.71  \n",
       "..    ...   ...     ...    ...   ...   ...    ...  \n",
       "472   0.0   0.0  4647.0  100.0  1.76  69.5  22.44  \n",
       "473   6.0  50.0  7042.0  100.0  1.77  76.0  24.26  \n",
       "474  13.0  85.0  5755.0  100.0  1.81  65.0  19.84  \n",
       "475  11.0  76.0  5688.0  100.0  1.72  51.7  17.48  \n",
       "476   0.0   0.0     0.0    0.0  0.00   0.0    NaN  \n",
       "\n",
       "[477 rows x 17 columns]"
      ]
     },
     "execution_count": 77,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "boy[cols]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "id": "ffdfbbc6",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Text(0.5, 1.0, '男生体重指数')"
      ]
     },
     "execution_count": 41,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 900x600 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize=(9,6))\n",
    "plt.rcParams['font.family']='Kaiti'  # 全局设置\n",
    "plt.rcParams['font.size']=18\n",
    "\n",
    "(boy['BMI_score'].value_counts()).plot(kind = 'pie', autopct = \"%0.2f%%\")\n",
    "\n",
    "plt.title('男生体重指数',fontsize = 32,weight='bold', color='white',backgroundcolor='#c5b783',pad = 25)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 74,
   "id": "e6b4b17f",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:>"
      ]
     },
     "execution_count": 74,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "(boy['BMI_score'].value_counts()).plot(kind = 'bar')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "09f80cad",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "id": "8a781915",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Index(['班级', '性别', '女800米跑', '女50米跑', '女跳远', '女体前屈', '女仰卧', '女肺活量', '身高', '体重',\n",
       "       'BMI'],\n",
       "      dtype='object')"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "girl.columns "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "id": "925b1ae9",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead tr th {\n",
       "        text-align: left;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr>\n",
       "      <th></th>\n",
       "      <th colspan=\"2\" halign=\"left\">男肺活量</th>\n",
       "      <th colspan=\"2\" halign=\"left\">女肺活量</th>\n",
       "      <th colspan=\"2\" halign=\"left\">男50米跑</th>\n",
       "      <th colspan=\"2\" halign=\"left\">女50米跑</th>\n",
       "      <th colspan=\"2\" halign=\"left\">男体前屈</th>\n",
       "      <th>...</th>\n",
       "      <th colspan=\"2\" halign=\"left\">女跳远</th>\n",
       "      <th colspan=\"2\" halign=\"left\">男引体</th>\n",
       "      <th colspan=\"2\" halign=\"left\">女仰卧</th>\n",
       "      <th colspan=\"2\" halign=\"left\">男1000米跑</th>\n",
       "      <th colspan=\"2\" halign=\"left\">女800米跑</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th></th>\n",
       "      <th>成绩</th>\n",
       "      <th>分数</th>\n",
       "      <th>成绩</th>\n",
       "      <th>分数</th>\n",
       "      <th>成绩</th>\n",
       "      <th>分数</th>\n",
       "      <th>成绩</th>\n",
       "      <th>分数</th>\n",
       "      <th>成绩</th>\n",
       "      <th>分数</th>\n",
       "      <th>...</th>\n",
       "      <th>成绩</th>\n",
       "      <th>分数</th>\n",
       "      <th>成绩</th>\n",
       "      <th>分数</th>\n",
       "      <th>成绩</th>\n",
       "      <th>分数</th>\n",
       "      <th>成绩</th>\n",
       "      <th>分数</th>\n",
       "      <th>成绩</th>\n",
       "      <th>分数</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>4540.0</td>\n",
       "      <td>100.0</td>\n",
       "      <td>3150.0</td>\n",
       "      <td>100.0</td>\n",
       "      <td>7.1</td>\n",
       "      <td>100.0</td>\n",
       "      <td>7.8</td>\n",
       "      <td>100.0</td>\n",
       "      <td>23.6</td>\n",
       "      <td>100.0</td>\n",
       "      <td>...</td>\n",
       "      <td>204.0</td>\n",
       "      <td>100.0</td>\n",
       "      <td>16.0</td>\n",
       "      <td>100.0</td>\n",
       "      <td>53.0</td>\n",
       "      <td>100.0</td>\n",
       "      <td>3.30</td>\n",
       "      <td>100.0</td>\n",
       "      <td>3.24</td>\n",
       "      <td>100.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>4420.0</td>\n",
       "      <td>95.0</td>\n",
       "      <td>3100.0</td>\n",
       "      <td>95.0</td>\n",
       "      <td>7.2</td>\n",
       "      <td>95.0</td>\n",
       "      <td>7.9</td>\n",
       "      <td>95.0</td>\n",
       "      <td>21.5</td>\n",
       "      <td>95.0</td>\n",
       "      <td>...</td>\n",
       "      <td>198.0</td>\n",
       "      <td>95.0</td>\n",
       "      <td>15.0</td>\n",
       "      <td>95.0</td>\n",
       "      <td>51.0</td>\n",
       "      <td>95.0</td>\n",
       "      <td>3.35</td>\n",
       "      <td>95.0</td>\n",
       "      <td>3.30</td>\n",
       "      <td>95.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>4300.0</td>\n",
       "      <td>90.0</td>\n",
       "      <td>3050.0</td>\n",
       "      <td>90.0</td>\n",
       "      <td>7.3</td>\n",
       "      <td>90.0</td>\n",
       "      <td>8.0</td>\n",
       "      <td>90.0</td>\n",
       "      <td>19.4</td>\n",
       "      <td>90.0</td>\n",
       "      <td>...</td>\n",
       "      <td>192.0</td>\n",
       "      <td>90.0</td>\n",
       "      <td>14.0</td>\n",
       "      <td>90.0</td>\n",
       "      <td>49.0</td>\n",
       "      <td>90.0</td>\n",
       "      <td>3.40</td>\n",
       "      <td>90.0</td>\n",
       "      <td>3.36</td>\n",
       "      <td>90.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>4050.0</td>\n",
       "      <td>85.0</td>\n",
       "      <td>2900.0</td>\n",
       "      <td>85.0</td>\n",
       "      <td>7.4</td>\n",
       "      <td>85.0</td>\n",
       "      <td>8.3</td>\n",
       "      <td>85.0</td>\n",
       "      <td>17.2</td>\n",
       "      <td>85.0</td>\n",
       "      <td>...</td>\n",
       "      <td>185.0</td>\n",
       "      <td>85.0</td>\n",
       "      <td>13.0</td>\n",
       "      <td>85.0</td>\n",
       "      <td>46.0</td>\n",
       "      <td>85.0</td>\n",
       "      <td>3.47</td>\n",
       "      <td>85.0</td>\n",
       "      <td>3.43</td>\n",
       "      <td>85.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>3800.0</td>\n",
       "      <td>80.0</td>\n",
       "      <td>2750.0</td>\n",
       "      <td>80.0</td>\n",
       "      <td>7.5</td>\n",
       "      <td>80.0</td>\n",
       "      <td>8.6</td>\n",
       "      <td>80.0</td>\n",
       "      <td>15.0</td>\n",
       "      <td>80.0</td>\n",
       "      <td>...</td>\n",
       "      <td>178.0</td>\n",
       "      <td>80.0</td>\n",
       "      <td>12.0</td>\n",
       "      <td>80.0</td>\n",
       "      <td>43.0</td>\n",
       "      <td>80.0</td>\n",
       "      <td>3.55</td>\n",
       "      <td>80.0</td>\n",
       "      <td>3.50</td>\n",
       "      <td>80.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>5 rows × 24 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "     男肺活量           女肺活量        男50米跑        女50米跑         男体前屈         ...  \\\n",
       "       成绩     分数      成绩     分数    成绩     分数    成绩     分数    成绩     分数  ...   \n",
       "0  4540.0  100.0  3150.0  100.0   7.1  100.0   7.8  100.0  23.6  100.0  ...   \n",
       "1  4420.0   95.0  3100.0   95.0   7.2   95.0   7.9   95.0  21.5   95.0  ...   \n",
       "2  4300.0   90.0  3050.0   90.0   7.3   90.0   8.0   90.0  19.4   90.0  ...   \n",
       "3  4050.0   85.0  2900.0   85.0   7.4   85.0   8.3   85.0  17.2   85.0  ...   \n",
       "4  3800.0   80.0  2750.0   80.0   7.5   80.0   8.6   80.0  15.0   80.0  ...   \n",
       "\n",
       "     女跳远          男引体          女仰卧        男1000米跑        女800米跑         \n",
       "      成绩     分数    成绩     分数    成绩     分数      成绩     分数     成绩     分数  \n",
       "0  204.0  100.0  16.0  100.0  53.0  100.0    3.30  100.0   3.24  100.0  \n",
       "1  198.0   95.0  15.0   95.0  51.0   95.0    3.35   95.0   3.30   95.0  \n",
       "2  192.0   90.0  14.0   90.0  49.0   90.0    3.40   90.0   3.36   90.0  \n",
       "3  185.0   85.0  13.0   85.0  46.0   85.0    3.47   85.0   3.43   85.0  \n",
       "4  178.0   80.0  12.0   80.0  43.0   80.0    3.55   80.0   3.50   80.0  \n",
       "\n",
       "[5 rows x 24 columns]"
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "stand.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "6b2d791a",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "id": "9dfe9ae1",
   "metadata": {},
   "outputs": [],
   "source": [
    "for col in [ '女800米跑', '女50米跑']:\n",
    "    \n",
    "   s = stand[col]   # 获取成绩的标准\n",
    "    \n",
    "   def convert(x):\n",
    "       for i in range(len(s)):   # 获取长度循环\n",
    "           if x <=s['成绩'].iloc[0]:\n",
    "               if x == 0:       # 判断是否没有成绩\n",
    "                   return 0    \n",
    "               return 100 \n",
    "           elif x > s['成绩'].iloc[-1]:\n",
    "               return 0     # 跑得太慢\n",
    "           elif (x > s['成绩'].iloc[i - 1]) and (x <= s['成绩'].iloc[i]):\n",
    "               return s['分数'].iloc[i]\n",
    "        \n",
    "         \n",
    "   girl[col + '成绩'] = girl[col].map(convert)   # 增加一列\n",
    "    "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "id": "1be7f227",
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>班级</th>\n",
       "      <th>性别</th>\n",
       "      <th>女800米跑</th>\n",
       "      <th>女50米跑</th>\n",
       "      <th>女跳远</th>\n",
       "      <th>女体前屈</th>\n",
       "      <th>女仰卧</th>\n",
       "      <th>女肺活量</th>\n",
       "      <th>身高</th>\n",
       "      <th>体重</th>\n",
       "      <th>BMI</th>\n",
       "      <th>女800米跑成绩</th>\n",
       "      <th>女50米跑成绩</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1</td>\n",
       "      <td>女</td>\n",
       "      <td>3.22</td>\n",
       "      <td>9.32</td>\n",
       "      <td>185.0</td>\n",
       "      <td>16.0</td>\n",
       "      <td>48.0</td>\n",
       "      <td>3775.0</td>\n",
       "      <td>163.0</td>\n",
       "      <td>51.3</td>\n",
       "      <td>0.0</td>\n",
       "      <td>100.0</td>\n",
       "      <td>72.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1</td>\n",
       "      <td>女</td>\n",
       "      <td>4.59</td>\n",
       "      <td>11.44</td>\n",
       "      <td>148.0</td>\n",
       "      <td>9.0</td>\n",
       "      <td>29.0</td>\n",
       "      <td>3683.0</td>\n",
       "      <td>163.0</td>\n",
       "      <td>66.6</td>\n",
       "      <td>0.0</td>\n",
       "      <td>40.0</td>\n",
       "      <td>10.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>1</td>\n",
       "      <td>女</td>\n",
       "      <td>3.46</td>\n",
       "      <td>13.40</td>\n",
       "      <td>150.0</td>\n",
       "      <td>7.0</td>\n",
       "      <td>40.0</td>\n",
       "      <td>3331.0</td>\n",
       "      <td>157.0</td>\n",
       "      <td>60.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>80.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>1</td>\n",
       "      <td>女</td>\n",
       "      <td>3.39</td>\n",
       "      <td>9.52</td>\n",
       "      <td>172.0</td>\n",
       "      <td>21.0</td>\n",
       "      <td>46.0</td>\n",
       "      <td>3701.0</td>\n",
       "      <td>160.0</td>\n",
       "      <td>50.7</td>\n",
       "      <td>0.0</td>\n",
       "      <td>85.0</td>\n",
       "      <td>70.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>1</td>\n",
       "      <td>女</td>\n",
       "      <td>3.43</td>\n",
       "      <td>9.79</td>\n",
       "      <td>145.0</td>\n",
       "      <td>8.0</td>\n",
       "      <td>34.0</td>\n",
       "      <td>3592.0</td>\n",
       "      <td>167.0</td>\n",
       "      <td>63.9</td>\n",
       "      <td>0.0</td>\n",
       "      <td>85.0</td>\n",
       "      <td>68.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   班级 性别  女800米跑  女50米跑    女跳远  女体前屈   女仰卧    女肺活量     身高    体重  BMI  \\\n",
       "0   1  女    3.22   9.32  185.0  16.0  48.0  3775.0  163.0  51.3  0.0   \n",
       "1   1  女    4.59  11.44  148.0   9.0  29.0  3683.0  163.0  66.6  0.0   \n",
       "2   1  女    3.46  13.40  150.0   7.0  40.0  3331.0  157.0  60.0  0.0   \n",
       "3   1  女    3.39   9.52  172.0  21.0  46.0  3701.0  160.0  50.7  0.0   \n",
       "4   1  女    3.43   9.79  145.0   8.0  34.0  3592.0  167.0  63.9  0.0   \n",
       "\n",
       "   女800米跑成绩  女50米跑成绩  \n",
       "0     100.0     72.0  \n",
       "1      40.0     10.0  \n",
       "2      80.0      0.0  \n",
       "3      85.0     70.0  \n",
       "4      85.0     68.0  "
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "girl.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "d0ac20ab",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "2a644182",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "id": "73e4f956",
   "metadata": {},
   "outputs": [],
   "source": [
    "for col in ['女跳远', '女体前屈', '女仰卧', '女肺活量']:\n",
    "   s = stand[col]\n",
    "    \n",
    "    \n",
    "   def convert(x):\n",
    "       for i in range(len(s)):\n",
    "           if x >= s['成绩'].iloc[i]:\n",
    "               return s['分数'].iloc[i]\n",
    "       return 0\n",
    "    \n",
    "   girl[col + '成绩'] = girl[col].map(convert)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "id": "7095d302",
   "metadata": {},
   "outputs": [],
   "source": [
    "cols = ['班级', '性别', '女800米跑','女800米跑成绩', '女50米跑', '女50米跑成绩', '女跳远', '女跳远成绩','女体前屈',\n",
    "        '女体前屈成绩', '女仰卧','女仰卧成绩', '女肺活量', '女肺活量成绩', '身高','体重', 'BMI']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "id": "8c32364d",
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "      <th>女跳远成绩</th>\n",
       "      <th>女体前屈</th>\n",
       "      <th>女体前屈成绩</th>\n",
       "      <th>女仰卧</th>\n",
       "      <th>女仰卧成绩</th>\n",
       "      <th>女肺活量</th>\n",
       "      <th>女肺活量成绩</th>\n",
       "      <th>身高</th>\n",
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       "      <th>BMI</th>\n",
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       "  </thead>\n",
       "  <tbody>\n",
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       "      <td>女</td>\n",
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       "      <th>1</th>\n",
       "      <td>1</td>\n",
       "      <td>女</td>\n",
       "      <td>4.59</td>\n",
       "      <td>40.0</td>\n",
       "      <td>11.44</td>\n",
       "      <td>10.0</td>\n",
       "      <td>148.0</td>\n",
       "      <td>60.0</td>\n",
       "      <td>9.0</td>\n",
       "      <td>66.0</td>\n",
       "      <td>29.0</td>\n",
       "      <td>66.0</td>\n",
       "      <td>3683.0</td>\n",
       "      <td>100.0</td>\n",
       "      <td>163.0</td>\n",
       "      <td>66.6</td>\n",
       "      <td>0.0</td>\n",
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       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>1</td>\n",
       "      <td>女</td>\n",
       "      <td>3.46</td>\n",
       "      <td>80.0</td>\n",
       "      <td>13.40</td>\n",
       "      <td>0.0</td>\n",
       "      <td>150.0</td>\n",
       "      <td>60.0</td>\n",
       "      <td>7.0</td>\n",
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       "      <td>40.0</td>\n",
       "      <td>76.0</td>\n",
       "      <td>3331.0</td>\n",
       "      <td>100.0</td>\n",
       "      <td>157.0</td>\n",
       "      <td>60.0</td>\n",
       "      <td>0.0</td>\n",
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       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>1</td>\n",
       "      <td>女</td>\n",
       "      <td>3.39</td>\n",
       "      <td>85.0</td>\n",
       "      <td>9.52</td>\n",
       "      <td>70.0</td>\n",
       "      <td>172.0</td>\n",
       "      <td>76.0</td>\n",
       "      <td>21.0</td>\n",
       "      <td>90.0</td>\n",
       "      <td>46.0</td>\n",
       "      <td>85.0</td>\n",
       "      <td>3701.0</td>\n",
       "      <td>100.0</td>\n",
       "      <td>160.0</td>\n",
       "      <td>50.7</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>1</td>\n",
       "      <td>女</td>\n",
       "      <td>3.43</td>\n",
       "      <td>85.0</td>\n",
       "      <td>9.79</td>\n",
       "      <td>68.0</td>\n",
       "      <td>145.0</td>\n",
       "      <td>50.0</td>\n",
       "      <td>8.0</td>\n",
       "      <td>64.0</td>\n",
       "      <td>34.0</td>\n",
       "      <td>70.0</td>\n",
       "      <td>3592.0</td>\n",
       "      <td>100.0</td>\n",
       "      <td>167.0</td>\n",
       "      <td>63.9</td>\n",
       "      <td>0.0</td>\n",
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       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
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       "      <td>...</td>\n",
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       "      <td>...</td>\n",
       "      <td>...</td>\n",
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       "      <th>588</th>\n",
       "      <td>17</td>\n",
       "      <td>女</td>\n",
       "      <td>3.51</td>\n",
       "      <td>78.0</td>\n",
       "      <td>9.60</td>\n",
       "      <td>70.0</td>\n",
       "      <td>150.0</td>\n",
       "      <td>60.0</td>\n",
       "      <td>24.0</td>\n",
       "      <td>95.0</td>\n",
       "      <td>41.0</td>\n",
       "      <td>78.0</td>\n",
       "      <td>2255.0</td>\n",
       "      <td>70.0</td>\n",
       "      <td>158.0</td>\n",
       "      <td>49.0</td>\n",
       "      <td>0.0</td>\n",
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       "    <tr>\n",
       "      <th>589</th>\n",
       "      <td>17</td>\n",
       "      <td>女</td>\n",
       "      <td>4.00</td>\n",
       "      <td>76.0</td>\n",
       "      <td>10.18</td>\n",
       "      <td>64.0</td>\n",
       "      <td>150.0</td>\n",
       "      <td>60.0</td>\n",
       "      <td>13.0</td>\n",
       "      <td>72.0</td>\n",
       "      <td>36.0</td>\n",
       "      <td>72.0</td>\n",
       "      <td>2937.0</td>\n",
       "      <td>85.0</td>\n",
       "      <td>161.0</td>\n",
       "      <td>55.7</td>\n",
       "      <td>0.0</td>\n",
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       "    <tr>\n",
       "      <th>590</th>\n",
       "      <td>17</td>\n",
       "      <td>女</td>\n",
       "      <td>3.45</td>\n",
       "      <td>80.0</td>\n",
       "      <td>10.18</td>\n",
       "      <td>64.0</td>\n",
       "      <td>152.0</td>\n",
       "      <td>62.0</td>\n",
       "      <td>15.0</td>\n",
       "      <td>76.0</td>\n",
       "      <td>35.0</td>\n",
       "      <td>72.0</td>\n",
       "      <td>2592.0</td>\n",
       "      <td>76.0</td>\n",
       "      <td>165.0</td>\n",
       "      <td>48.6</td>\n",
       "      <td>0.0</td>\n",
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       "    <tr>\n",
       "      <th>591</th>\n",
       "      <td>17</td>\n",
       "      <td>女</td>\n",
       "      <td>4.01</td>\n",
       "      <td>74.0</td>\n",
       "      <td>9.67</td>\n",
       "      <td>68.0</td>\n",
       "      <td>165.0</td>\n",
       "      <td>70.0</td>\n",
       "      <td>10.0</td>\n",
       "      <td>68.0</td>\n",
       "      <td>41.0</td>\n",
       "      <td>78.0</td>\n",
       "      <td>1829.0</td>\n",
       "      <td>60.0</td>\n",
       "      <td>154.0</td>\n",
       "      <td>43.6</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>592</th>\n",
       "      <td>17</td>\n",
       "      <td>女</td>\n",
       "      <td>4.48</td>\n",
       "      <td>50.0</td>\n",
       "      <td>9.09</td>\n",
       "      <td>74.0</td>\n",
       "      <td>180.0</td>\n",
       "      <td>80.0</td>\n",
       "      <td>10.0</td>\n",
       "      <td>68.0</td>\n",
       "      <td>46.0</td>\n",
       "      <td>85.0</td>\n",
       "      <td>2962.0</td>\n",
       "      <td>85.0</td>\n",
       "      <td>162.0</td>\n",
       "      <td>55.3</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>593 rows × 17 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "     班级 性别  女800米跑  女800米跑成绩  女50米跑  女50米跑成绩    女跳远  女跳远成绩  女体前屈  女体前屈成绩  \\\n",
       "0     1  女    3.22     100.0   9.32     72.0  185.0   85.0  16.0    76.0   \n",
       "1     1  女    4.59      40.0  11.44     10.0  148.0   60.0   9.0    66.0   \n",
       "2     1  女    3.46      80.0  13.40      0.0  150.0   60.0   7.0    64.0   \n",
       "3     1  女    3.39      85.0   9.52     70.0  172.0   76.0  21.0    90.0   \n",
       "4     1  女    3.43      85.0   9.79     68.0  145.0   50.0   8.0    64.0   \n",
       "..   .. ..     ...       ...    ...      ...    ...    ...   ...     ...   \n",
       "588  17  女    3.51      78.0   9.60     70.0  150.0   60.0  24.0    95.0   \n",
       "589  17  女    4.00      76.0  10.18     64.0  150.0   60.0  13.0    72.0   \n",
       "590  17  女    3.45      80.0  10.18     64.0  152.0   62.0  15.0    76.0   \n",
       "591  17  女    4.01      74.0   9.67     68.0  165.0   70.0  10.0    68.0   \n",
       "592  17  女    4.48      50.0   9.09     74.0  180.0   80.0  10.0    68.0   \n",
       "\n",
       "      女仰卧  女仰卧成绩    女肺活量  女肺活量成绩     身高    体重  BMI  \n",
       "0    48.0   85.0  3775.0   100.0  163.0  51.3  0.0  \n",
       "1    29.0   66.0  3683.0   100.0  163.0  66.6  0.0  \n",
       "2    40.0   76.0  3331.0   100.0  157.0  60.0  0.0  \n",
       "3    46.0   85.0  3701.0   100.0  160.0  50.7  0.0  \n",
       "4    34.0   70.0  3592.0   100.0  167.0  63.9  0.0  \n",
       "..    ...    ...     ...     ...    ...   ...  ...  \n",
       "588  41.0   78.0  2255.0    70.0  158.0  49.0  0.0  \n",
       "589  36.0   72.0  2937.0    85.0  161.0  55.7  0.0  \n",
       "590  35.0   72.0  2592.0    76.0  165.0  48.6  0.0  \n",
       "591  41.0   78.0  1829.0    60.0  154.0  43.6  0.0  \n",
       "592  46.0   85.0  2962.0    85.0  162.0  55.3  0.0  \n",
       "\n",
       "[593 rows x 17 columns]"
      ]
     },
     "execution_count": 18,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "girl[cols]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 123,
   "id": "c1209217",
   "metadata": {},
   "outputs": [],
   "source": [
    "# 计算体重"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "id": "0e15bd25",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0     163.0\n",
       "1     163.0\n",
       "2     157.0\n",
       "3     160.0\n",
       "4     167.0\n",
       "5     170.0\n",
       "6     158.0\n",
       "7     160.0\n",
       "8     160.0\n",
       "9     171.0\n",
       "10    157.0\n",
       "11    175.0\n",
       "12    164.0\n",
       "13    159.0\n",
       "14    165.0\n",
       "15    156.0\n",
       "16    172.0\n",
       "17    167.0\n",
       "18    157.0\n",
       "19    167.0\n",
       "20    157.0\n",
       "21    172.0\n",
       "22    155.0\n",
       "23    161.0\n",
       "24    162.0\n",
       "25    166.0\n",
       "26    161.0\n",
       "27    160.0\n",
       "28    169.0\n",
       "29    165.0\n",
       "30    163.0\n",
       "31    159.0\n",
       "32    155.0\n",
       "33    169.0\n",
       "34    162.0\n",
       "35    166.0\n",
       "36    169.0\n",
       "37    167.0\n",
       "38    160.0\n",
       "39    163.0\n",
       "40    165.0\n",
       "41    154.0\n",
       "42    159.0\n",
       "43    159.0\n",
       "44    171.0\n",
       "45    159.0\n",
       "46    167.0\n",
       "47    158.0\n",
       "48    174.0\n",
       "49    160.0\n",
       "Name: 身高, dtype: float64"
      ]
     },
     "execution_count": 19,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "h = girl['身高'] \n",
    "h[:50]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "ccfb3fa7",
   "metadata": {},
   "outputs": [],
   "source": [
    "# 计算身高"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "id": "52c5e989",
   "metadata": {},
   "outputs": [],
   "source": [
    "def convert(x):\n",
    "   if x >100:\n",
    "       return x/100\n",
    "   return x\n",
    "girl['身高'] = girl['身高'].map(convert)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "id": "258fe274",
   "metadata": {},
   "outputs": [],
   "source": [
    "girl['BMI'] = (girl['体重'] / girl['身高']**2).round(2)  # 保留2位小数"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "73b63ad4",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "id": "ff23f825",
   "metadata": {},
   "outputs": [],
   "source": [
    "def convert_BMI(x):\n",
    "   if x >= 25.3:\n",
    "       return 60\n",
    "   elif (x <= 16.4) or (x >22.8 and x < 25.2):\n",
    "       return 80\n",
    "   elif x >= 16.5 and x <= 22.7:\n",
    "       return 100\n",
    "   return 0\n",
    "girl['BMI_score'] = girl['BMI'].map(convert_BMI)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "id": "67482009",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>班级</th>\n",
       "      <th>性别</th>\n",
       "      <th>女800米跑</th>\n",
       "      <th>女50米跑</th>\n",
       "      <th>女跳远</th>\n",
       "      <th>女体前屈</th>\n",
       "      <th>女仰卧</th>\n",
       "      <th>女肺活量</th>\n",
       "      <th>身高</th>\n",
       "      <th>体重</th>\n",
       "      <th>BMI</th>\n",
       "      <th>女800米跑成绩</th>\n",
       "      <th>女50米跑成绩</th>\n",
       "      <th>女跳远成绩</th>\n",
       "      <th>女体前屈成绩</th>\n",
       "      <th>女仰卧成绩</th>\n",
       "      <th>女肺活量成绩</th>\n",
       "      <th>BMI_score</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1</td>\n",
       "      <td>女</td>\n",
       "      <td>3.22</td>\n",
       "      <td>9.32</td>\n",
       "      <td>185.0</td>\n",
       "      <td>16.0</td>\n",
       "      <td>48.0</td>\n",
       "      <td>3775.0</td>\n",
       "      <td>1.63</td>\n",
       "      <td>51.3</td>\n",
       "      <td>19.31</td>\n",
       "      <td>100.0</td>\n",
       "      <td>72.0</td>\n",
       "      <td>85.0</td>\n",
       "      <td>76.0</td>\n",
       "      <td>85.0</td>\n",
       "      <td>100.0</td>\n",
       "      <td>100</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1</td>\n",
       "      <td>女</td>\n",
       "      <td>4.59</td>\n",
       "      <td>11.44</td>\n",
       "      <td>148.0</td>\n",
       "      <td>9.0</td>\n",
       "      <td>29.0</td>\n",
       "      <td>3683.0</td>\n",
       "      <td>1.63</td>\n",
       "      <td>66.6</td>\n",
       "      <td>25.07</td>\n",
       "      <td>40.0</td>\n",
       "      <td>10.0</td>\n",
       "      <td>60.0</td>\n",
       "      <td>66.0</td>\n",
       "      <td>66.0</td>\n",
       "      <td>100.0</td>\n",
       "      <td>80</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>1</td>\n",
       "      <td>女</td>\n",
       "      <td>3.46</td>\n",
       "      <td>13.40</td>\n",
       "      <td>150.0</td>\n",
       "      <td>7.0</td>\n",
       "      <td>40.0</td>\n",
       "      <td>3331.0</td>\n",
       "      <td>1.57</td>\n",
       "      <td>60.0</td>\n",
       "      <td>24.34</td>\n",
       "      <td>80.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>60.0</td>\n",
       "      <td>64.0</td>\n",
       "      <td>76.0</td>\n",
       "      <td>100.0</td>\n",
       "      <td>80</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>1</td>\n",
       "      <td>女</td>\n",
       "      <td>3.39</td>\n",
       "      <td>9.52</td>\n",
       "      <td>172.0</td>\n",
       "      <td>21.0</td>\n",
       "      <td>46.0</td>\n",
       "      <td>3701.0</td>\n",
       "      <td>1.60</td>\n",
       "      <td>50.7</td>\n",
       "      <td>19.80</td>\n",
       "      <td>85.0</td>\n",
       "      <td>70.0</td>\n",
       "      <td>76.0</td>\n",
       "      <td>90.0</td>\n",
       "      <td>85.0</td>\n",
       "      <td>100.0</td>\n",
       "      <td>100</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>1</td>\n",
       "      <td>女</td>\n",
       "      <td>3.43</td>\n",
       "      <td>9.79</td>\n",
       "      <td>145.0</td>\n",
       "      <td>8.0</td>\n",
       "      <td>34.0</td>\n",
       "      <td>3592.0</td>\n",
       "      <td>1.67</td>\n",
       "      <td>63.9</td>\n",
       "      <td>22.91</td>\n",
       "      <td>85.0</td>\n",
       "      <td>68.0</td>\n",
       "      <td>50.0</td>\n",
       "      <td>64.0</td>\n",
       "      <td>70.0</td>\n",
       "      <td>100.0</td>\n",
       "      <td>80</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   班级 性别  女800米跑  女50米跑    女跳远  女体前屈   女仰卧    女肺活量    身高    体重    BMI  \\\n",
       "0   1  女    3.22   9.32  185.0  16.0  48.0  3775.0  1.63  51.3  19.31   \n",
       "1   1  女    4.59  11.44  148.0   9.0  29.0  3683.0  1.63  66.6  25.07   \n",
       "2   1  女    3.46  13.40  150.0   7.0  40.0  3331.0  1.57  60.0  24.34   \n",
       "3   1  女    3.39   9.52  172.0  21.0  46.0  3701.0  1.60  50.7  19.80   \n",
       "4   1  女    3.43   9.79  145.0   8.0  34.0  3592.0  1.67  63.9  22.91   \n",
       "\n",
       "   女800米跑成绩  女50米跑成绩  女跳远成绩  女体前屈成绩  女仰卧成绩  女肺活量成绩  BMI_score  \n",
       "0     100.0     72.0   85.0    76.0   85.0   100.0        100  \n",
       "1      40.0     10.0   60.0    66.0   66.0   100.0         80  \n",
       "2      80.0      0.0   60.0    64.0   76.0   100.0         80  \n",
       "3      85.0     70.0   76.0    90.0   85.0   100.0        100  \n",
       "4      85.0     68.0   50.0    64.0   70.0   100.0         80  "
      ]
     },
     "execution_count": 26,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "girl.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "id": "c5f94e50",
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "      <th></th>\n",
       "      <th>班级</th>\n",
       "      <th>性别</th>\n",
       "      <th>女800米跑</th>\n",
       "      <th>女800米跑成绩</th>\n",
       "      <th>女50米跑</th>\n",
       "      <th>女50米跑成绩</th>\n",
       "      <th>女跳远</th>\n",
       "      <th>女跳远成绩</th>\n",
       "      <th>女体前屈</th>\n",
       "      <th>女体前屈成绩</th>\n",
       "      <th>女仰卧</th>\n",
       "      <th>女仰卧成绩</th>\n",
       "      <th>女肺活量</th>\n",
       "      <th>女肺活量成绩</th>\n",
       "      <th>身高</th>\n",
       "      <th>体重</th>\n",
       "      <th>BMI</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1</td>\n",
       "      <td>女</td>\n",
       "      <td>3.22</td>\n",
       "      <td>100.0</td>\n",
       "      <td>9.32</td>\n",
       "      <td>72.0</td>\n",
       "      <td>185.0</td>\n",
       "      <td>85.0</td>\n",
       "      <td>16.0</td>\n",
       "      <td>76.0</td>\n",
       "      <td>48.0</td>\n",
       "      <td>85.0</td>\n",
       "      <td>3775.0</td>\n",
       "      <td>100.0</td>\n",
       "      <td>1.63</td>\n",
       "      <td>51.3</td>\n",
       "      <td>19.31</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1</td>\n",
       "      <td>女</td>\n",
       "      <td>4.59</td>\n",
       "      <td>40.0</td>\n",
       "      <td>11.44</td>\n",
       "      <td>10.0</td>\n",
       "      <td>148.0</td>\n",
       "      <td>60.0</td>\n",
       "      <td>9.0</td>\n",
       "      <td>66.0</td>\n",
       "      <td>29.0</td>\n",
       "      <td>66.0</td>\n",
       "      <td>3683.0</td>\n",
       "      <td>100.0</td>\n",
       "      <td>1.63</td>\n",
       "      <td>66.6</td>\n",
       "      <td>25.07</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>1</td>\n",
       "      <td>女</td>\n",
       "      <td>3.46</td>\n",
       "      <td>80.0</td>\n",
       "      <td>13.40</td>\n",
       "      <td>0.0</td>\n",
       "      <td>150.0</td>\n",
       "      <td>60.0</td>\n",
       "      <td>7.0</td>\n",
       "      <td>64.0</td>\n",
       "      <td>40.0</td>\n",
       "      <td>76.0</td>\n",
       "      <td>3331.0</td>\n",
       "      <td>100.0</td>\n",
       "      <td>1.57</td>\n",
       "      <td>60.0</td>\n",
       "      <td>24.34</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>1</td>\n",
       "      <td>女</td>\n",
       "      <td>3.39</td>\n",
       "      <td>85.0</td>\n",
       "      <td>9.52</td>\n",
       "      <td>70.0</td>\n",
       "      <td>172.0</td>\n",
       "      <td>76.0</td>\n",
       "      <td>21.0</td>\n",
       "      <td>90.0</td>\n",
       "      <td>46.0</td>\n",
       "      <td>85.0</td>\n",
       "      <td>3701.0</td>\n",
       "      <td>100.0</td>\n",
       "      <td>1.60</td>\n",
       "      <td>50.7</td>\n",
       "      <td>19.80</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>1</td>\n",
       "      <td>女</td>\n",
       "      <td>3.43</td>\n",
       "      <td>85.0</td>\n",
       "      <td>9.79</td>\n",
       "      <td>68.0</td>\n",
       "      <td>145.0</td>\n",
       "      <td>50.0</td>\n",
       "      <td>8.0</td>\n",
       "      <td>64.0</td>\n",
       "      <td>34.0</td>\n",
       "      <td>70.0</td>\n",
       "      <td>3592.0</td>\n",
       "      <td>100.0</td>\n",
       "      <td>1.67</td>\n",
       "      <td>63.9</td>\n",
       "      <td>22.91</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>588</th>\n",
       "      <td>17</td>\n",
       "      <td>女</td>\n",
       "      <td>3.51</td>\n",
       "      <td>78.0</td>\n",
       "      <td>9.60</td>\n",
       "      <td>70.0</td>\n",
       "      <td>150.0</td>\n",
       "      <td>60.0</td>\n",
       "      <td>24.0</td>\n",
       "      <td>95.0</td>\n",
       "      <td>41.0</td>\n",
       "      <td>78.0</td>\n",
       "      <td>2255.0</td>\n",
       "      <td>70.0</td>\n",
       "      <td>1.58</td>\n",
       "      <td>49.0</td>\n",
       "      <td>19.63</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>589</th>\n",
       "      <td>17</td>\n",
       "      <td>女</td>\n",
       "      <td>4.00</td>\n",
       "      <td>76.0</td>\n",
       "      <td>10.18</td>\n",
       "      <td>64.0</td>\n",
       "      <td>150.0</td>\n",
       "      <td>60.0</td>\n",
       "      <td>13.0</td>\n",
       "      <td>72.0</td>\n",
       "      <td>36.0</td>\n",
       "      <td>72.0</td>\n",
       "      <td>2937.0</td>\n",
       "      <td>85.0</td>\n",
       "      <td>1.61</td>\n",
       "      <td>55.7</td>\n",
       "      <td>21.49</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>590</th>\n",
       "      <td>17</td>\n",
       "      <td>女</td>\n",
       "      <td>3.45</td>\n",
       "      <td>80.0</td>\n",
       "      <td>10.18</td>\n",
       "      <td>64.0</td>\n",
       "      <td>152.0</td>\n",
       "      <td>62.0</td>\n",
       "      <td>15.0</td>\n",
       "      <td>76.0</td>\n",
       "      <td>35.0</td>\n",
       "      <td>72.0</td>\n",
       "      <td>2592.0</td>\n",
       "      <td>76.0</td>\n",
       "      <td>1.65</td>\n",
       "      <td>48.6</td>\n",
       "      <td>17.85</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>591</th>\n",
       "      <td>17</td>\n",
       "      <td>女</td>\n",
       "      <td>4.01</td>\n",
       "      <td>74.0</td>\n",
       "      <td>9.67</td>\n",
       "      <td>68.0</td>\n",
       "      <td>165.0</td>\n",
       "      <td>70.0</td>\n",
       "      <td>10.0</td>\n",
       "      <td>68.0</td>\n",
       "      <td>41.0</td>\n",
       "      <td>78.0</td>\n",
       "      <td>1829.0</td>\n",
       "      <td>60.0</td>\n",
       "      <td>1.54</td>\n",
       "      <td>43.6</td>\n",
       "      <td>18.38</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>592</th>\n",
       "      <td>17</td>\n",
       "      <td>女</td>\n",
       "      <td>4.48</td>\n",
       "      <td>50.0</td>\n",
       "      <td>9.09</td>\n",
       "      <td>74.0</td>\n",
       "      <td>180.0</td>\n",
       "      <td>80.0</td>\n",
       "      <td>10.0</td>\n",
       "      <td>68.0</td>\n",
       "      <td>46.0</td>\n",
       "      <td>85.0</td>\n",
       "      <td>2962.0</td>\n",
       "      <td>85.0</td>\n",
       "      <td>1.62</td>\n",
       "      <td>55.3</td>\n",
       "      <td>21.07</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>593 rows × 17 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "     班级 性别  女800米跑  女800米跑成绩  女50米跑  女50米跑成绩    女跳远  女跳远成绩  女体前屈  女体前屈成绩  \\\n",
       "0     1  女    3.22     100.0   9.32     72.0  185.0   85.0  16.0    76.0   \n",
       "1     1  女    4.59      40.0  11.44     10.0  148.0   60.0   9.0    66.0   \n",
       "2     1  女    3.46      80.0  13.40      0.0  150.0   60.0   7.0    64.0   \n",
       "3     1  女    3.39      85.0   9.52     70.0  172.0   76.0  21.0    90.0   \n",
       "4     1  女    3.43      85.0   9.79     68.0  145.0   50.0   8.0    64.0   \n",
       "..   .. ..     ...       ...    ...      ...    ...    ...   ...     ...   \n",
       "588  17  女    3.51      78.0   9.60     70.0  150.0   60.0  24.0    95.0   \n",
       "589  17  女    4.00      76.0  10.18     64.0  150.0   60.0  13.0    72.0   \n",
       "590  17  女    3.45      80.0  10.18     64.0  152.0   62.0  15.0    76.0   \n",
       "591  17  女    4.01      74.0   9.67     68.0  165.0   70.0  10.0    68.0   \n",
       "592  17  女    4.48      50.0   9.09     74.0  180.0   80.0  10.0    68.0   \n",
       "\n",
       "      女仰卧  女仰卧成绩    女肺活量  女肺活量成绩    身高    体重    BMI  \n",
       "0    48.0   85.0  3775.0   100.0  1.63  51.3  19.31  \n",
       "1    29.0   66.0  3683.0   100.0  1.63  66.6  25.07  \n",
       "2    40.0   76.0  3331.0   100.0  1.57  60.0  24.34  \n",
       "3    46.0   85.0  3701.0   100.0  1.60  50.7  19.80  \n",
       "4    34.0   70.0  3592.0   100.0  1.67  63.9  22.91  \n",
       "..    ...    ...     ...     ...   ...   ...    ...  \n",
       "588  41.0   78.0  2255.0    70.0  1.58  49.0  19.63  \n",
       "589  36.0   72.0  2937.0    85.0  1.61  55.7  21.49  \n",
       "590  35.0   72.0  2592.0    76.0  1.65  48.6  17.85  \n",
       "591  41.0   78.0  1829.0    60.0  1.54  43.6  18.38  \n",
       "592  46.0   85.0  2962.0    85.0  1.62  55.3  21.07  \n",
       "\n",
       "[593 rows x 17 columns]"
      ]
     },
     "execution_count": 27,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "girl[cols]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "id": "26ceaa1f",
   "metadata": {},
   "outputs": [],
   "source": [
    "from matplotlib import pyplot as plt"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "id": "0c9b1062",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Text(0.5, 1.0, '女生体重指数')"
      ]
     },
     "execution_count": 36,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 900x600 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize=(9,6))\n",
    "plt.rcParams['font.family']='Kaiti'  # 全局设置\n",
    "plt.rcParams['font.size']=18\n",
    "\n",
    "(girl['BMI_score'].value_counts()).plot(kind = 'pie', autopct = \"%0.2f%%\")\n",
    "\n",
    "plt.title('女生体重指数',fontsize = 32,weight='bold', color='white',backgroundcolor='#c5b783',pad = 25)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "3392bcf4",
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3 (ipykernel)",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.7.6"
  },
  "toc": {
   "base_numbering": 1,
   "nav_menu": {},
   "number_sections": true,
   "sideBar": true,
   "skip_h1_title": false,
   "title_cell": "Table of Contents",
   "title_sidebar": "Contents",
   "toc_cell": false,
   "toc_position": {},
   "toc_section_display": true,
   "toc_window_display": false
  }
 },
 "nbformat": 4,
 "nbformat_minor": 5
}
